text:
\section{Introduction}
\label{introduction}
The modern technological breakthroughs within \ac{IoT} and \ac{AI} and their application in healthcare are enhancing the quality of life and increasing the longevity of life~\cite{qadri2020future,dimitrov2016medical,9294145}. For instance, with the current COVID-19 pandemic putting new pressures and creating a demand on healthcare, automated solutions through \ac{AI} and \ac{IoT} can help provide treatment and assisting medical doctors or healthcare workers \cite{allam2020coronavirus}. Such AI and IoT-assisted solutions couldn't have come at a better time. Medical samples such as imaging and pathological data are being used to teach the machines how to search for particular disease symptoms and patterns. The machines then learn how to identify and predict diseases in the human body accurately and precisely.

\indent Data-driven medical application has emerged as a promising tool for designing reliable and scalable predictive algorithms from medical data and has attracted a great deal of attention from academia to industry~\cite{cai2019survey}. This has undoubtedly improved the quality of healthcare delivery~\cite{cascini2021developing}. However, such data-driven medical applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and \ac{QoS} requirements. Moreover, the data owners, i.e., individuals, patients, and hospitals, are concerned about sharing their private data with an external third-party entity. These discussions are ongoing, and there is a need for innovative models for data sharing. Therefore, strict legislation such as the United States Consumer Privacy Bill of Rights and the European Commission's General Data Protection Regulations (GDPR) has been formed to protect users' privacy~\cite{andrew2021general,hartzog2020privacy,yuan2019policy,hoofnagle2019european}.

\begin{figure*}
	\centering
	\includegraphics[width= 7.1 in]{images/FL_Taxonomy_final.png}
	\caption{Taxonomy of the topics covered in this work.}
	\label{taxonomy}
\end{figure*}

\begin{table
\centering
\caption{Abbreviations used in our paper.}
\def1.5{1.07}
\begin{tabular}{l|l}
\hline
\textbf{Acronym} & \hspace*{\fill} \textbf{Definition} \hspace*{\fill} \\ \hline
AI & Artificial Intelligence \\ \hline
ARX & Autoregression with Exogenous Variable \\ \hline
ASD & Autism Spectrum Disorder \\ \hline
CFL & Clustered Federated Learning \\ \hline
CILL & Cyclic Institutional Incremental Learning \\ \hline
CNN & Convolutional Neural Networks \\ \hline
CT & Computed Tomography \\ \hline
DDQN & Double Deep Q-Network \\ \hline
DL & Deep Learning \\ \hline
DNet & Distributed Net \\ \hline
DP & Differential Privacy \\ \hline
Edge-FL & Edge-based Federated Learning \\ \hline
EHR & Electronic Health Record \\ \hline
EMRs & Electronic Medical Records \\ \hline
EU & European Union \\ \hline
FADL & Federated Autonomous Deep Learning \\ \hline
FAIR & Findable, Accessible, Interoperable, Reusable \\ \hline
FAMHE & Federated Analytics-based Multiparty Homomorphic  \\
& Encryption \\ \hline
FATE & Federated AI Technology Enabler Framework \\ \hline
FATHOM & Federated Multi-task Hierarchical Attention Mode \\ \hline
FeARH & Federated Machine Learning with Anonymous Random  \\
& Hybridization \\ \hline
FedAvg & Federated Averaging \\ \hline
FL & Federated Learning \\ \hline
FML & Federated Machine Learning \\ \hline
FDL & Federated Deep Learning \\ \hline
FOCUS & Federated Opportunistic Computing for Ubiquitous System \\ \hline
FUALA & Federated Uncertainty-Aware Learning Algorithm \\ \hline
FCM & Fuzzy Cognitive Maps \\ \hline
GCAE & Generative Convolutional Autoencoder \\ \hline
GDPR & General Data Protection Regulation \\ \hline
I.I.D & Identical and Independently Distributed \\ \hline
ILL & Institutional Incremental Learning \\ \hline
IoMT & Internet of Medical Things \\ \hline
IoT & Internet of Things \\ \hline
IPFS & Interplanetary File System \\ \hline
MEC & Mobile Edge Computing \\ \hline
ML & Machine Learning \\ \hline
MPC & Multiparty Computation \\ \hline
MRI & Magnetic Resonance Imaging \\ \hline
NER & Named Entity Recognition \\ \hline
NLP & Natural Language Processing \\ \hline
Open-FL & Open Federated Learning \\ \hline
PHT & Personal Health Train \\ \hline
PROM & Patient-reported Outcomes Measurements \\ \hline
QoS & Quality of Service \\ \hline
SCBFL & Stochastic Channel-based Federated Learning \\ \hline
TFF & Tensor Flow Federated \\ \hline
UCADI & Unified Computed Tomography AI Diagnostic Initiative \\ \hline
\end{tabular}
\end{table}

\indent Data are usually stored and processed on the cloud servers in a data center located far away from the edge devices. Generally, on the cloud server, ML/DL models are being applied to the data for analysis and designing reliable and scalable models~\cite{john2020developing}. To train a machine learning model, one usually needs to move all of the data to a single machine or to a cluster of machines in a data center. This is obviously a challenge in the context of medical/healthcare data. Training ML models centrally for medical data can lead to impediments to privacy. People or users may not want to disclose their health information to any other external entity. A healthcare provider or hospital may refuse to exchange medical data with other healthcare providers or hospitals. Furthermore, healthcare practitioners and hospitals are not completely free to exchange patient data with pharmaceutical corporations. There are also practical engineering issues to consider. A massive amount of important training data is generated on technology such as smartphones, smartwatches, and wearable computers at the edges of slow and unreliable network~\cite{xu2020edge}. Massive data communication with such devices to a centralized server or data center can be time-consuming and expensive. Therefore, considering the real-time and low-latency concerns of the applications, storing, processing, and analyzing the data in the centralized cloud is not feasible. As a result, the current data storage and analysis trend are moving from the centralized cloud server to the distributed devices located at the edge of the networks where the data are generated. Similarly, Edge computing has emerged as a key enabling technology for processing the data at the edge of the network in real-time~\cite{yu2017survey,chen2018thriftyedge,rafique2020complementing}. Compared to centralized cloud computing, processing the data at the edge of the network has advantages in minimizing the delay, privacy, and security concerns~\cite{sha2020survey,zhang2020stec,shi2016edge}.\\
\indent To amalgamate the benefits of edge computing and AI, EdgeAI processes AI algorithms on edge, that is, on the edge users' devices~\cite{li2019edge,rausch2019towards,shi2020communication}. EdgeAI uses the device's  hardware to process data and performs ML/DL operations. For many medical applications, a simple sensor-to-cloud architecture is not viable~\cite{ray2019edge}. Moreover, European Union (EU) GDPR strictly prohibits the storage of patient data outside of the hospital~\cite{richter2019patient}. Relying exclusively on remote data centers may also be unsuitable due to patient safety in the event of network and data center outages.

\begin{table*}
\caption{Comparison and overview of recent surveys in FL.}
\label{tbl_related_works}
\centering
\begin{tabular}{ l|l}
 \midrule
 \textbf{Publications} & \hspace*{\fill} \textbf{Main Research Focus and Scope} \hspace*{\fill}  \\
 \midrule
 This Survey & ~~\llap{\textbullet}~~ Presents a comprehensive survey on the use of FL in the medical domain. \\
 & ~~\llap{\textbullet}~~ Provides a holistic taxonomy on the use of several technologies in conjugation with FL, especially for medical applications. \\
 & ~~\llap{\textbullet}~~ Covers the state-of-the-art works targeting the most commonly caused cancer both in terms of incidence and mortality and the \\
 & \hspace{2.9ex} application of FL in developing computer-aided diagnosis tools. \\
 & ~~\llap{\textbullet}~~ Discusses the main challenges, open research problems, and future research directions of FL in the context of the medical domain. \\
 \midrule
 Ref~\cite{abdulrahman2020survey} & ~~\llap{\textbullet}~~ Focuses on comparing different ML-based deployment architectures for FL.   \\
 & ~~\llap{\textbullet}~~ Covers privacy and security, and resource management.  \\
\midrule
 Ref~\cite{aledhari2020federated} & ~~\llap{\textbullet}~~ Focuses on FL enabling technologies, protocols, and applications.  \\
 & ~~\llap{\textbullet}~~ Provides a summary of the relevant FL protocols, platforms, and some real-life use-cases for FL.  \\
\midrule
 Ref~\cite{ji2021emerging} & ~~\llap{\textbullet}~~ Focuses on exploring learning algorithms to improve the federated averaging algorithm. \\
 & ~~\llap{\textbullet}~~ Reviews model fusion methods for FL. \\
 \midrule
 Ref~\cite{li2020review} & ~~\llap{\textbullet}~~ Summarizes the development prospects of FL in industrial field.\\
 \midrule
 Ref~\cite{li2021survey} & ~~\llap{\textbullet}~~ Explores the definition of FL systems and analyzed the FL system components. \\
 & ~~\llap{\textbullet}~~ Categorizes FL systems according to data distribution, ML model, privacy mechanism, communication architecture,  \\
 & \hspace{2.9ex}scale of federation and motivation of federation. \\
 \midrule
 Ref~\cite{lim2020federated} & ~~\llap{\textbullet}~~ Surveys the applications of FL for mobile edge computing network optimization. \\
 \midrule
 Ref~\cite{01973} & ~~\llap{\textbullet}~~ Investigates industrial application trends of FL, essential factors affecting the quality of FL models, and compares FL and \\
 & \hspace{2.9ex}non-FL algorithms in terms of learning quality. \\
 \midrule
 Ref~\cite{rieke2020future} & ~~\llap{\textbullet}~~ Investigates FL as a potential solution for the future of digital health and highlights the challenges of FL on digital health.\\
 \midrule
 Ref~\cite{zheng2022applications} & ~~\llap{\textbullet}~~ Discusses the application of FL for various fields in smart cities, including communication security and privacy issues. \\
 \midrule
 Ref~\cite{pfitzner2021federated} & ~~\llap{\textbullet}~~ Provides a systematic literature review on the FL and its applicability for confidential healthcare datasets.\\
 \midrule
 Ref~\cite{xu2021federated,ali2022federated} & ~~\llap{\textbullet}~~ Describes the role of FL in Internet of Medical Things (IoMT) networks for privacy preservation. \\
 \midrule
 Ref~\cite{antunes2022federated} & ~~\llap{\textbullet}~~ Presents a systematic literature review on current research about FL in the context of EHR data.\\
 & ~~\llap{\textbullet}~~ Discusses a general architecture for FL based on the primary findings from the literature analysis.\\
 \midrule
\end{tabular}
\vspace{-5mm}
\end{table*}

\indent EdgeAI enables enhanced ML models and resources to be leveraged on each device as the storage and processing capabilities of devices within distributed networks grow. This has fueled interest in \ac{FL}, which leverages training \ac{ML} models directly on remote edge devices~\cite{lim2020federated,wang2019edge,wang2019adaptive}. \ac{EHR}, for example, have already prepared healthcare organizations to use Big Data tools for next-generation data analytics~\cite{luo2016big,lv2017next,maier2017surgical}. AI/ML tools undoubtedly bring a value to this and have the potential to revolutionize this field~\cite{rasouli2021artificial}. They are projecting to improve automation quality and intelligent decision-making in primary/tertiary patient care and public healthcare systems~\cite{mintz2019introduction,davenport2019potential,fogel2018artificial}. In this regard, FL in the current technological era is a distributed AI system that allows models to be distributively trained by averaging local model updates across multiple hospitals/healthcare datasets without access to local data or EHR~\cite{rieke2020future,xu2021federated}. By employing the FL technique, possible risks of revealing patients' sensitive health information can be addressed. Furthermore, the FL strategy increases the training performance on health data by exploiting big datasets and processing resources from the individual local devices in the network, which would not be achievable with the centralized AI technique.

\subsection{Scope and Contributions}
FL has already been used in several domains such as edge/cloud computing, IoT, blockchains, etc. There is a growing number of research papers on FL in conjugation with several of these technologies that are currently published by researchers from academia and industry. We explain these works in the next section highlighting how FL technologies have been applied, especially in healthcare settings.\\
\indent Health-related data are very sensitive and difficult to obtain since their usage is strictly regulated. Even if data anonymization could circumvent these constraints, researchers across academia, industries, and health regulatory bodies have widely acknowledged that deleting metadata such as a patient's name or related information is not sufficient enough to protect and preserve user privacy. Therefore, FL has emerged as a novel distributed AI paradigm that seeks to overcome the problem of medical data governance and privacy. FL collectively trains algorithms on the device itself without exchanging the data with external entities.

Most of the existing survey studies such as~\cite{abdulrahman2020survey,aledhari2020federated,ji2021emerging,li2020review,li2021survey,lim2020federated,rieke2020future,zheng2022applications} are confined to explaining either general architecture, models, security, and privacy algorithms for FL or explaining how FL can be used in conjugation with other emerging technologies. Since FL has been successfully applied in conjugation with other emerging technologies, there is hardly a few survey work or studies such as \cite{pfitzner2021federated,xu2021federated,ali2022federated,antunes2022federated} that have looked into FL and providing an overview on works that have tackled especially in the medical domain. Therefore, the focus of this survey is solely on the integration of FL with other emerging technologies for medical applications. This survey article differs from other similar survey studies in the FL area in a way that it offers comprehensive taxonomy on the application of several emerging technologies in conjunction with FL, particularly for medical applications. A taxonomy of the discussed topics in this survey paper is shown in Fig.~\ref{taxonomy}. Moreover, a detailed comparison and overview of existing related surveys in FL are shown in Table~\ref{tbl_related_works}. Before explaining FL and its applications with other emerging technologies in the context of healthcare that is carried out in Section III, we also review and outline a concise summary of recent studies on FL and their categorization in the healthcare domain in Table III.

\indent The following are the primary contributions of our work relative to the recent literature:

\begin{itemize}
\item Current literature focuses on the technical rigor of the FL. In this study, however, we provide a comprehensive survey on the use of FL, especially for medical applications.

\item We outline the importance of several emerging technologies that can be used alongside FL to tackle medical applications' problems.

\item We cover the recent works on the global cancer burden (targeting most commonly caused cancer both in terms of incidence and mortality) and the application of \ac{FL} in the development of computer-aided diagnosis tools.

\item We provide a brief overview and comparison of existing open-source FL software frameworks.

\item We present key challenges of FL from the medical application perspective.

\item Finally, this survey highlights the open problems of FL and offers future research directions from a healthcare standpoint.
\end{itemize}

\subsection{Paper Organizations}
The rest of the paper is organized as follows. In Section~\ref{concepts_and_framework}, we briefly give an overview of the FL concept and frameworks for FL in the medical domain. We explain the existing works in FL for medical applications in conjugation with several other technologies in Section~\ref{FL-Context-of-Healthcare}. Open-source FL software frameworks are presented in Section~\ref{open_source_FL_Software}. We examine the challenges of FL from a medical perspective in Section~\ref{opportunities_and_challenges_FL}. Some open research problems and future research directions of FL in medical applications are presented in Section~\ref{Open_Problems_FL}. Finally, in Section~\ref{conclusion}, we provide concluding remarks.

\begin{table*}[htbp]
\caption{Summary of recent studies on FL in Healthcare.} \label{summar_recent_FL_Healthcare}

\begin{subtable}{1\textwidth}
\centering
\begin{tabular}{*{3}{p{\dimexpr0.33\linewidth-2\tabcolsep\relax}}}
\toprule
\textbf{Reference} & \textbf{Key idea} & \textbf{Category}\\
\midrule
Ref~\cite{andreux2020siloed} & Introduced local-statistic batch normalization layers to \ac{DL} architectures, resulting in a unique FL strategy that produced a cooperatively trained yet center-specific model. & FL Data Heterogeneity, Federated Transfer Learning, Medical Image Analysis   \\
        \hline
Ref~\cite{beguier2021differentially} & Genomics data divided across two virtual centers were used to train a supervised model for the prediction of the occurrence of breast cancer, while differential privacy was used to preserve data privacy. & Privacy Algorithm, Cancer Prediction \\
\hline
Ref~\cite{boughorbel2019federated} & Federated Uncertainty-Aware Learning Algorithm (FUALA), a novel FL model that enhances Federated Averaging (FedAvg) in the context of EHR, has been proposed. & FL Algorithm, FL on EHR Data \\
\hline
Ref~\cite{brophy2021estimation} & Provided a decentralized FL framework for producing estimations of mean arterial pressure and continuous data on arterial blood pressure using a single optical sensor. & FL Framework, Blood Pressure Estimation \\
\hline
Ref~\cite{can2021privacy} & Applied privacy-preserving FL on biomedical informatics data collected with wearable IoT devices. & FL in Bio-medical IoT, Stress Level Monitoring \\
\hline
Ref~\cite{chen2020vafl} & Introduced a novel technique that enables each client to perform stochastic gradient algorithms independently of the others, making it appropriate for clients with intermittent connectivity. & Bio-medical Data, Data Heterogeneity in FL, Convergence \\
\hline
Ref~\cite{chen2020focus} & To solve the issue of data disparity, the Federated Opportunistic Computing for Ubiquitous System (FOCUS) was proposed. A minimal number of benchmark samples are kept on the server by FOCUS. The label quality of a client's dataset is computed using a unique mutual cross-entropy-based credibility score that does not require access to the raw data. & Data Heterogeneity in FL, Bio-medical Datasets \\
\hline
Ref~\cite{choudhury2019differential} & Introduced an FL-based differential privacy framework that allows distributed health data stored locally at several sites with two degrees of privacy protection that can be used to develop a global model. & Differential Privacy Framework, Health Data \\
\hline
Ref~\cite{cui2021fearh} & Proposed Federated \ac{ML} with anonymous random hybridization (FeARH), which is a distributed \ac{ML} training method that aims to deal with untrustworthy parties in federated \ac{ML} by implementing a hybrid algorithm by adding randomization into parameter sets shared among various parties. & Security, FL in EHR Data \\
\hline
Ref~\cite{flores2021federated} & Cooperated on a healthcare FL project to develop the ``EXAM" (EMR CXR AI Model) model, which used inputs from vital signs, laboratory data, and chest x-rays to predict future oxygen requirements for SARS-COV-2 patients. & FL in Pandemic Scenario \\
\hline
Ref~\cite{froelicher2021truly} & Using a modular system based on multiparty homomorphic encryption, a Federated Analytics-based Multiparty Homomorphic Encryption (FAMHE) is proposed that enables privacy-preserving execution of federated analytics workflows yielding precise results without leaking any intermediate information. & Security \& Privacy of FL, Bio-medical Data \\
\hline
Ref~\cite{ge2020fedner} & Proposed an FL-based strategy for medical named entity recognition (NER) that protects patient privacy and uses labeled data from multiple platforms to speed up NER model training without requiring platforms to exchange raw data. & Medical Data Labelling, Privacy \\
\hline
Ref~\cite{banerjee2020multi} & Proposed multi-diseases classification from chest-X-ray using Federated \ac{DL} (FDL) & Fl-based Disease Classification, Federated Transfer Learning \\
\hline
Ref~\cite{brisimi2018federated} & A \ac{FL} system is suggested that enables peer-to-peer learning of prediction models without the sharing of raw EHR data. & FL on EHR Data, Prediction of Heart-related Hospitalizations \\

\hline
\end{tabular}
\end{subtable}
\end{table*}

\pagebreak

\begin{table*}[htbp]
\ContinuedFloat
\caption{Summary of recent studies on FL in Healthcare, continued.}
\begin{subtable}{1\textwidth}
\centering
\begin{tabular}{*{3}{p{\dimexpr0.33\linewidth-2\tabcolsep\relax}}}
\toprule
\textbf{Reference} & \textbf{Key idea} & \textbf{Category}\\
\hline
Ref~\cite{chang2021privacy} & Reviewed the methods used for multi-institutional applications to distributed FL learning, handle data heterogeneity, and protect patient privacy. & Privacy, Multi-institutional Patient Data \\
\hline

Ref~\cite{chen2020achieving} & Used a non-interactive key generation technique to propose a privacy-preserving FL framework that is compatible with irrelevant updates using a communication-efficient secure aggregation protocol. & Privacy, FL on EHR Data\\
\hline

Ref~\cite{choudhury2020personal} & Proposed a method for calculating cohort statistics in a federated manner while protecting patient privacy and determining the number of patients with hypertension. & Privacy Framework, Hypertension \\
\hline
Ref~\cite{choudhury2019predicting,estiri2019federated} & Introduced an FL system that uses distributed health data stored locally at different sites to train a global  model. & FL Algorithm, FL on EHR Data \\
\hline
Ref~\cite{dang2020building} & Reported the results of pilot research using FL to create a model of in-hospital mortality. Empirically, it demonstrated that FL achieved a comparable level of performance to that of centralized training, with the added benefit of preventing the exchange of datasets between hospitals. & Federated Transfer Learning, In-Hospital Mortality Prediction, FL Algorithms\\
\hline
Ref~\cite{deist2020distributed} & Proposed a Personal Health Train (PHT) that connects FAIR (Findable, Accessible, Interoperable, Reusable) data sources, enables distributed data analysis, and uses \ac{ML} techniques. & Privacy Framework, FL on Lung Cancer Patient-Specific Database \\
\hline
Ref~\cite{favela2016towards,sadilek2021privacy} & Proposed and suggested creating an open-access federated dataset repository for sharing and preservation. & Privacy of FL, Epidemiology\\
\hline
Ref~\cite{grama2020robust} & Implemented and assessed several aggregation techniques for healthcare data in FL. They also demonstrated how these techniques might be used during training to identify and eliminate bad or malicious local clients.  & FL Algorithm, Medical Diagnosis Classification Task\\
\hline
Ref~\cite{hashmani2021adaptive} & Presented an adaptive ensemble CNN as the primary classifier for a federated \ac{ML} model of skin disease. & Medical Imaging, Skin Disease Detection\\
\hline
Ref~\cite{huang2020loadaboost} & Proposed an adaptive boosting method named LoAdaBoost and investigated the performance of learning in IID and non-IID data distribution scenarios. & Data Heterogeneity in FL, Intensive Care Dataset \\
\hline
Ref~\cite{ju2020privacy} & Outlined and predicted stroke risk and implemented the FL model on cloud servers while protecting user privacy. The federated prediction model system asynchronously enables numerous client connections and arbitrary local gradient iterations in each communication round. & Privacy Framework for FL, Stroke Prediction\\
\hline
Ref~\cite{kulkarni2021dnet} & Proposed the Distributed Net (DNet) method to handle the privacy concerns of distributed datasets, which poses its own set of difficulties due to high communication latency, performance, and efficiency. & FL Algorithm, Bio-medical Datasets \\
\hline
Ref~\cite{leedata} & Used three benchmark datasets, including a clinical benchmark dataset, to assess the performance and reliability of FL. & FL Data Distributions, FL Bio-medical Datasets\\
\hline
Ref~\cite{liu2018fadl} & Proposed a new technique called Federated-Autonomous Deep Learning (FADL), which trains some components of the model utilizing data from several sources in a distributed fashion. & FL Training, FL on EHR Data\\
\hline
Ref~\cite{lu2019learn,lu2020decentralized} & Focused on increasing the effectiveness of communication for completely decentralized \ac{FL} across a graph, where the algorithm first conducts local updates over a number of iterations before enabling communications between the nodes. & FL Communication, FL on EHR Data \\
\hline
Ref~\cite{ma2021pocket} & Designed a secure \ac{FL} method with different keys to prevent data island-level & Security and Privacy Framework for FL, \\
\hline
\end{tabular}
\end{subtable}
\end{table*}

\pagebreak

\begin{table*}[htbp]
\ContinuedFloat
\caption{Summary of recent studies on FL in Healthcare, continued.}
\begin{subtable}{1\textwidth}
\centering
\begin{tabular}{*{3}{p{\dimexpr0.33\linewidth-2\tabcolsep\relax}}}
\toprule
\textbf{Reference} & \textbf{Key idea} & \textbf{Category}\\
\hline
 & poisoning attacks on patient data for medical diagnosis. & FL-based Medical Diagnosis\\
\hline
Ref~\cite{mitra2020federated} & Discussed how FL may be used to support flexible AI models trained on biopharma and clinical unstructured data, with an emphasis on extracting useful information using Natural Language Processing (NLP). & FL Training, FL Dataset\\
\hline
Ref~\cite{mohan2017broker} & Used a federated healthcare cloud broker architecture, which combines independent health service providers to create a large healthcare federation, satisfies user needs by sharing resources at various locations at reasonable costs, and offers Quality of Service (QoS) to all users. In the suggested federated healthcare cloud broker architecture, a variety of trust mechanisms, including reputation-based trust, SLA verification trust, and policy-based trust, are calculated to guarantee the security and privacy of users who access the services. & Security and Privacy Framework, Healthcare-based FL Architecture\\
\hline
Ref~\cite{pedrosa2019pseudonymisation} & Proposed a pseudonymization protocol built on a $(t, n)$-threshold secret sharing system and public-key cryptography for health records in federated services. & Security Framework, FL on EHR Data \\
\hline
Ref~\cite{pezoulas2020dealing} & Developed disease prediction models using FL on harmonized cohort data stored in private cloud databases using a computational process that deploys data curation and ontology matching to curate and aligns heterogeneous data structures. & Data Heterogeneity in FL, Disease Prediction Model  \\
\hline
Ref~\cite{salmeron2020privacy} & Developed a training scheme for collaborative Fuzzy Cognitive Maps (FCM) learning that delivers data privacy compliance and introduced a novel approach for distributed learning of particle swarm optimization-based FCM in a manner that protects privacy. & FL Training, FL Optimization, Cancer Prediction\\
\hline
Ref~\cite{shao2020stochastic} & A stochastic channel-based \ac{FL} (SCBFL) method that enables users to train a high-performance model jointly and in a distributed way was developed to address security and privacy challenges. & FL Algorithm, FL Training, Bio-medical Data \\
\hline
Ref~\cite{stephens2016implementing} & To structure iterations of development and facilitate knowledge sharing between two health network development teams coordination to support and manage shared phases, a spiral model for implementation and assessment was utilized. & Federated Data Sharing Architectures, FL on EHR Data\\
\hline
Ref~\cite{fang2020bayesian} & To predict heart rate using an autoregression with an exogenous variable (ARX) model, Bayesian inference FL has been presented. & FL Algorithm, Heart Rate Prediction\\
\hline
Ref~\cite{thwal2021attention} & Suggested a personalized deep learning-based FL-trained and FL-managed clinical decision support system to aid healthcare practitioners in diagnosis & FL Algorithm, Security and Privacy Framework, Bio-medical Datasets \\
\hline
Ref~\cite{tourani2020consensus} & Presented consensus federated modeling, which enables collaboration between a small health system and a larger system to create a model specific to the smaller system without sharing any data instances. & Federated Transfer Learning, Healthcare Systems\\
\hline
Ref~\cite{xue2021resource} & A FL model that integrates mobile-edge computing (MEC) and software-defined networking has been proposed to take advantage of the computation and storage resources available among edge nodes when deploying a double deep Q-network (DDQN) in order to produce a stable and sequential clinical treatment policy. & Decentralized FL Framework, Security Algorithms, Clinical Decision Support Systems, FL on EHR Data\\
\hline
Ref~\cite{jordan2020patient} & For patient-reported outcomes measurements (PROM) data, a FL method that ensures privacy is proposed. & FL Algorithm, Privacy Framework, FL on EHR Data\\
\hline
\end{tabular}
\end{subtable}
\end{table*}

\begin{figure*} [h!]
\centering
\includegraphics[width= 6.5 in]{images/Federated_Learning_Architecture.pdf}
\caption{Classical architecture of \ac{FL}. The clients send local model updates trained using the local dataset to the server for aggregation. The central server finally aggregates the local models transmitted by the participating clients and sends the current updated global model back to each participating client.}
\label {Federated_Learning_Architecture}
\end{figure*}

\begin{comment}
\begin{itemize}
\item Why data-driven ML/DL/AI is needed for the Medical field?
\item How AI-aided disruptive innovations are transforming this field?
\item How do AI algorithms learn to predict and make decisions? Can we entrust them with decisions that affect our lives and health?
\item Considering the scenario of the European ageing population, what is being considered so far? What are the security and privacy implications?
\item Explain about Electronics Health Records (EHR) and how it is being used in hospitals or clinics?
\item Explain medical data security and privacy implications. Talk about medical data fragmentation issues.
\item Why is \ac{FL} needed? Explain its popularity as an attractive data-driven solution for the medical field.
\item Explain how \ac{FL} can be used to tackle the current pandemic COVID-19 and future pandemics with some related works.
\item Explain why this survey paper is needed and what will it cover?
\item How is it different from current surveys on \ac{FL}?
\item Explain contributions, open problems, challenges, and future research directions.
\end{itemize}
\end{comment}

\begin{figure*} [htb]
\centering
\includegraphics[width= 6.9 in]{images/Healthcare_FL_Framework.png}
\caption{FL framework for the medical domain.}
\label {FL_Framework}
\end{figure*}

\section{FL Concepts and Framework for FL in the Medical Domain}
\label{concepts_and_framework}

A classical \ac{FL} architecture is shown in Fig.~\ref{Federated_Learning_Architecture}. It consists of a centralized global server that broadcasts the AI/ML model parameters to the clients in the FL network. The centralized server could either select the clients randomly or use a client selection algorithm~\cite{yu2020sustainable}. After receiving the global model parameters from the centralized global server, the selected clients train the model using their own local data. The clients then communicate their local model parameters to the server, which will be aggregated and used to create the global model on the server.\\
\indent \emph{FedAvg} is one of the first and most common FL aggregation algorithms initially proposed by Google~\cite{mcmahan2017communication}. The global server uses the FedAvg algorithm for aggregating the local parameters of different clients in each iteration~\cite{xia2021auto,li2019convergence}. This process is repeated until the desired number of iterations or convergence is reached on the global centralized server. When the data are non-iid, FedAvg works really well, but FedAvg on non-iid data lacks a theoretical guarantee in a convex optimization environment~\cite{li2019convergence}.\\
\indent A similar framework for FL in the medical domain is exemplified in Fig.~\ref{FL_Framework}, where mobile healthcare, hospital healthcare, and home healthcare can be potential clients. Such domains may differ in their data distributions. FL can be applied to these domains considering their data distributions to overcome security, privacy, healthcare systems, and device challenges. For example, mobile healthcare may consist of millions of smart healthcare devices such as smartwatches, smartphones, and electronic health gadgets that individuals use to monitor their health. Mobile healthcare has its own challenges regarding energy, resources, communication, computation, security, and privacy. On the other hand, hospital healthcare comprises larger hospitals with hundreds in number in a region. These larger hospitals have enough energy, resources, and computation capabilities and might not face issues such as frequent drop-out of clients and straggler problems that are more frequent in the FL networks containing a large number of mobile healthcare devices. Finally, home healthcare refers to various medical services and treatments that can be provided in the comfort of our homes. In most cases, home healthcare is less expensive, and it may be composed of thousands of smart healthcare devices in home settings. A remote patient monitoring solution falls under home healthcare, where a patient is being treated under personalized home care. Based on data distributions, the framework for FL in the medical domain can be primarily categorized into the frameworks described below.

\subsection{Horizontal FL Framework}
Horizontal FL employs datasets with the same feature space across all devices, implying that Medical Client A and Medical Client B have the same set of features. Two regional healthcare institutions, for example, may have diverse user groups from their separate regions, with a relatively limited intersection set of users. However, because of their relatively close health diagnoses, the feature spaces are identical~\cite{lu2022personalized}.\\
\indent The limited sample size in data training is solved by a horizontal FL data split. For example, Li et al. tested the autism spectrum disorder (ASD) prediction scenario with four medical institutions in four distinct locations~\cite{li2020multi}. Each medical party shares the same user features provided by the medical equipment, and all patient samples from four medical nodes are combined to train a global model in an FL setting.

\subsection{Vertical FL Framework}
Vertical FL collaborates to jointly train a global model from multiple feature spaces using a variety of datasets~\cite{romanini2021pyvertical}. In data training, vertical FL data division increases the feature dimension. For instance, a large hospital and a medical insurance company residing in the same location may share the same user's database. However, the feature spaces of these two organizations may be significantly different as a medical hospital focuses on the treatment history of the patients, and medical insurance institution focuses on the medical bills, receipts, and claims of the patients. Leveraging these two sets of datasets from distinct healthcare domains, vertical FL may better serve patients/users by using health-related data to deliver better health recommendations to those residing in that particular location. Cha et al. provided an example of this kind of vertical FL framework. For vertically partitioned medical data, the authors built an autoencoder FL model for transforming user features of each client into a latent dimension~\cite{cha2021implementing}.

\subsection{Federated Transfer Learning Framework}
Vertical FL with a pre-trained model learned on a comparable dataset for tackling a different issue is known as federated transfer learning. Federated transfer learning can be used when the two data sets differ not just in terms of samples but also in terms of feature space. It may be used to train a customized health model, such as making health recommendations based on the user's previous food and exercise habits. Here, food and exercising habits represent two completely different domains with distinct feature spaces. For instance, Chen et al. proposed \emph{FedHealth}, which aggregates the data from different organizations~\cite{chen2020fedhealth}. \emph{FedHealth} offers a personalized model for each organization using federated transfer learning. This FL model learns to categorize human activity recognition tasks first, then uses transfer learning to extend the classification task to Parkinson's disease categorization. Federated transfer learning creates a global model for a particular disease prediction in this situation, which could subsequently be applied to another medical challenge.

\section{FL and Its Applications with Other Technologies in the Context of Healthcare}
\label{FL-Context-of-Healthcare}

As its distinctive feature, FL allows the development of a robust and reliable \ac{ML} model across different domain applications without requiring the raw data to be shared with a central server. It can for example be potentially used in healthcare~\cite{rieke2020future, brisimi2018federated, huang2019patient}, \ac{NLP}~\cite{lin2021fednlp}, transportation~\cite{liu2020privacy, tan2020federated}, and finance~\cite{cheng2020federated}. Processing and securing the millions of petabytes of data produced by ubiquitous networked \ac{IoT} devices have become a challenge with the technological advances and growing popularity of \ac{IoT} and distributed applications. Maintaining the confidentiality and privacy of medical records is a challenging issue that has drawn the attention of researchers from the industry, academia, and medical research. \ac{FL} techniques have become the most widely used privacy preservation methods in industrial and medical AI applications. As a result, several recent research works across the \ac{ML} community have proven that employing \ac{FL} techniques could significantly increase efficiency and help reduce the burden on healthcare systems worldwide~\cite{shen2019medchain}. In addition, \ac{FL} models ensure a high level of robustness and effectiveness compared to the traditional data-driven medical applications~\cite{grama2020robust, fang2020local}.

To potentially increase its scope of application domains, \ac{FL} can be conveniently used with other technologies across different domains to accelerate collaborative training of \ac{ML} and \ac{DL} models without requiring data to be shared with a central server. In this section, we detail how \ac{FL} is being used in conjunction with other modern technologies in the context of the medical domain.

\subsection{Blockchain-based FL in Healthcare}
\label{blockchain-based-FL-in-Healthcare}

Instead of sharing the original data from clients, \ac{FL} protects the privacy of the raw data stored on each client by distributing local model updates such as gradient information, as shown in Fig.~\ref{Federated_Learning_Architecture}. As soon as receiving the local updates from the participating clients, the FL system aggregates the models transmitted by each client and sends back an updated global model. However, this poses some critical challenges (Section~\ref{opportunities_and_challenges_FL}). Combining FL frameworks with blockchain technology has the potential to enhance the underlying FL model's security, scalability, and performance. At a high level, blockchain is a powerful and emerging decentralized public ledger or database technology that enables devices to learn collaboratively without relying on a central aggregator~\cite{bao2019flchain, desai2021blockfla, ramanan2020baffle}. \\
\indent Blockchain technology has a wide range of potential applications and functions in different key data-driven areas, including in the healthcare industry. Traditionally, blockchain technology has been used to manage the transactional records of Bitcoin assets in an open network environment and other digital cryptocurrencies across a network~\cite{nakamoto2008bitcoin}. It has also been used in different domains such as transforming business and society~\cite{frizzo2020blockchain}, transport and logistics~\cite{pournader2020blockchain}, etc. In addition to this, its capabilities and applications can also be applied to various aspects of healthcare, including securely collecting and managing electronic health records~\cite{engelhardt2017hitching}. Several previous research studies have looked into the role of blockchain technology in the healthcare sector~\cite{agbo2019blockchain, griggs2018healthcare, holbl2018systematic, qadri2020future, zubaydi2019review, hasselgren2020blockchain, zubaydi2019review, vazirani2020blockchain}. As discussed in detail in~\cite{dasaklis2018blockchain, srivastava2019light, hossein2019blockchain, chakraborty2019secure}, blockchain technology can significantly improve the security of hospital information systems.\\
\indent Leveraging blockchain technology in digital health is one of the most promising techniques to securely store health records and efficiently deploy \ac{FL} applications in the healthcare industry. The authors in~\cite{passerat2019blockchain} have proposed a modern, secured, and fully decentralized architecture with privacy-preserving encryption techniques for \ac{FL} in the context of the healthcare sector built on the Ethereum blockchain that logs events in the underlying network without revealing the identities of patients. A hardware-based secure aggregation protocol and an encryption toolkit native to the Ethereum ecosystem are introduced in~\cite{passerat2019blockchain}. In addition to this, a symmetric Diffie-Helmann key exchange protocol established from the worker's public key and the secured aggregator's private key or vice versa is used to encrypt data between the secured central aggregator and a worker~\cite{passerat2019blockchain}. We believe secured architectures like this can be employed in AI-based healthcare applications with strong privacy and security requirements.\\
\indent Using decentralized data, models can be trained using the distributed FL approach~\cite{bonawitz2017practical}. Both \ac{FL} and blockchain techniques have a decentralized nature. Combining these two technologies could accelerate the development of AI and privacy-aware applications in the healthcare domain. For example, the authors in~\cite{polap2021agent} have developed a secured multi-agent system by combining the implementations of blockchain technology, and \ac{FL} approaches where the blockchain system provides data security. They claim that their proposed architecture offers solutions for the \ac{IoMT} by enabling the distribution of complicated tasks into individual agent units that can collaboratively cooperate.  For the simulation experiment, the authors used Skin Cancer MNIST: HAM10000 datasets~\cite{tschandl2018ham10000} divided into training and validation sets with the ratio of 70:30, respectively, resulting in the proposed architecture achieving an accuracy of 80\% for 25 iterations~\cite{polap2021agent}.\\
\indent Blockchain technology also provides a unique opportunity to advance the research in clinical trials and precision medicine~\cite{shae2018transform}. Jeffrey et al.~\cite{shae2018transform} proposed a new data-driven architecture that leverages FL and transfer learning approaches to transform blockchain smart contracts into a distributed parallel computing architecture for precision medicine. Such techniques are helpful for decentralized extensive medical data processing. Moreover, blockchains are being employed in the field of disease diagnosis. Furthermore, blockchains are being employed in the field of disease diagnosis. Chen et al.~\cite{chen2019asynchronous} built a general blockchain-based decentralized privacy-preserving cross-institution disease classification framework called Health-Chain. Health-Chain combines differential privacy and pseudo-identity mechanisms to address the data privacy issues. A distributed stochastic gradient descent algorithm and gradient compensation delay approaches are used to address the asynchronous problem of blockchain in the Health-Chain framework. The authors illustrate the efficiency and effectiveness of the proposed Health-Chain framework by conducting experiments using two popular disease recognition tasks, breast cancer diagnosis, and ECG arrhythmia classifications distributed over a set of entities~\cite{chen2019asynchronous}.

\subsection{FL Enabled Cloud Computing for IoT-based Healthcare Monitoring}
\label{FL-Cloud-Computing-IoT-Healthcare-Monitoring}

\ac{IoT} technology has revolutionized the world by providing humans with connectivity, interconnected smart devices, and productivity~\cite{care2016internet}. The evolution of \ac{IoT} and other \ac{IoT}-enabled smart system technologies is driven by the integration of billions of distributed smart devices and sensors that generate user data from real-time applications. One of the IoT application domains that has received much attention from the industry and academic research community is healthcare~\cite{dang2019survey}. The work presented in~\cite{dang2019survey} discusses different concepts and existing IoT and cloud computing applications in the healthcare domain. It also discusses the security challenges of IoT and cloud computing in healthcare systems and summarizes possible solutions.

Internet of Medical Things (IoMT) is another revolutionizing technology in the medical and healthcare industry context that involves the integration of smart devices~\cite{wang2022privacy}. With the proliferation of such technologies, integrated devices generate tremendous amounts of data daily. Without uploading and storing the raw data on a central server, data scientists can evaluate the generated data locally at the edge devices utilizing \ac{FL} techniques in a decentralized manner, ensuring the privacy of the \ac{IoT} device's data. Keeping personal data, for example, in the context of the healthcare sector, local data has substantial security and privacy benefits. This is one of the main differences between FL techniques and traditional centralized \ac{ML} methodologies. The authors of~\cite{zhao2020privacy} designed a system to assist home appliance manufacturers in evaluating data provided by \ac{IoT} devices by incorporating differential privacy which is used in combination with \ac{FL} and the blockchain for \ac{IoT} data used in smart home monitoring.

\subsection{Edge Computing Assisted FL for Healthcare}
\label{edge_computing_FL}

\begin{figure} [!t]
\centering
\includegraphics[width=\columnwidth,scale=0.99,keepaspectratio]{images/edge_computing.png}
\caption{General architecture of Edge Computing in healthcare.}
\label {edge_computing}
\end{figure}

Edge computing is an extension of the traditional cloud computing that brings compute and data storage closer to data sources, enhancing network resilience and availability while lowering latency and bandwidth~\cite{abreha2021monitoring, mao2017survey, mach2017mobile}. Fig.~\ref{edge_computing} shows the general architecture of an edge computing platform. Edge computing distributes the edge server's processing power, intelligence, and communication capabilities directly to the ubiquitous edge devices. This approach reduces the response time and the vast amount of data coming toward the Internet, which is beneficial for time-sensitive applications. The work in~\cite{wu2020fedhome} presents a novel cloud and edge-based FL framework, FedHome, for personalized in-home health monitoring by refining the model with a generated class-balanced dataset from the user's data. To address the statistical and communication challenges of FL, the authors implemented the underlying global cloud model as a \ac{GCAE} that utilizes a generated class-balanced dataset from a realistic human activity recognition data~\cite{wu2020fedhome}. Furthermore, the authors argue that \ac{GCAE} is a lightweight model for transferring data between the cloud and the edges, which helps reduce the communication cost of FL in FedHome by parameter sharing and dimension reduction. As reported in their paper, FedHome achieves a high accuracy of 95.41\%, with more than 7.49\% accuracy improvement over the widely-used \ac{CNN} techniques. It is also reported that FedHome enhances accuracy by more than 10\% when compared to other FL systems. Hence, the authors claim that FedHome can be employed as a powerful approach for in-home health monitoring in the future~\cite{wu2020fedhome}.

The design and validation of a fog computing-based prototype that accelerates the computation process of mobile patients by four times are presented in~\cite{garcia2020accelerating}. Another novel software framework called \emph{BodyEdge} that consists of a tiny mobile client module to be installed on mobile devices and an edge gateway supporting a multi-radio interface for reliable connectivity and multi-technology communication to collect and locally process data from different scenarios is proposed in~\cite{pace2018edge}. This healthcare framework focuses on improving latency and the overall reliability performance to support human-centric applications in local environments by reducing the data traffic toward the Internet. Two cardiac monitoring case studies have been implemented to detect high-stress scenarios in an industrial setting, such as automotive factory workers and athletes. The experimental case studies concluded that the response time meets the standards for healthcare \ac{IoT} applications~\cite{pace2018edge}.

\ac{FL} has the potential to serve as an enabling technology for collaborative model training of \ac{MEC} systems. It is mainly suited to edge computing applications because, by its design, edge computing avoids the need to move data from distributed edge devices to the central cloud platform. As a result, \ac{FL} can easily take advantage of the computational resources of edge servers and the data collected by the distributed edge devices. Here, we will address the real-world practicalities of FL in the context of the healthcare setting. The work in~\cite{hakak2020framework} introduces a personalized edge-assisted FL framework that supports healthcare analytics based on user-generated data. According to the authors, the framework leverages pre-trained models to extract user-tailored insights for analyzing diseases by monitoring mobility levels and behaviors using data generated from wearable devices while maintaining privacy and cloud resources~\cite{hakak2020framework}. As explained above, the edge computing setting can also be combined with other emerging technologies to maintain the integrity and improve the security of the distributed IoT devices. For example, the integration of FL and blockchain in edge computing and its design, including communication cost, resource allocation, incentive mechanism, security, and privacy protection issues, is presented in detail in~\cite{nguyen2021federated}. \\
\indent Recent advancements in blockchain and \ac{MEC} have created new opportunities for smart and federated healthcare revolutionization~\cite{rahman2018blockchain, asif2018toward, abdellatif2020sshealth, rahman2018spatial, kuo2017blockchain}. A new decentralized healthcare architecture for distributed and secured \ac{EMRs} sharing among federated hospitals based on blockchain and \ac{MEC} is proposed in~\cite{nguyen2020blockchain}. The authors designed a novel decentralized EMRs storage using an interplanetary file system (IPFS) platform on the \ac{MEC} servers to support secured and distributed data sharing. To improve computation offloading decisions in a MEC system, the authors in~\cite{wang2019edge} proposed employing deep reinforcement learning techniques and FL frameworks with mobile edge systems. Such approaches help IoT devices save more energy and maintain the quality of the expected services to deliver~\cite{ren2019federated}. The work proposed in~\cite{nguyen2021bedgehealth} is a novel decentralized health architecture that combines \ac{MEC} and blockchain for data sharing and offloading across federated hospital networks.\\
\indent Protecting personal data and maintaining its privacy is crucial in smart healthcare systems. The security of low-powered IoT devices and associated data is equally important in IoT-enabled healthcare systems. A lightweight privacy protection protocol for FL under an edge computing environment based on a shared secret and a weight mask to ensure users' privacy in a smart healthcare scenario is provided in~\cite{wang2022privacy}. According to the authors, the technique maintains gradient privacy without losing model accuracy and resists equipment dropping and collusion attacks between distributed devices~\cite{wang2022privacy}. The authors design an algorithm based on a digital signature and hash function to ensure the integrity and consistency of the messages while preventing replay attacks. Finally, the authors compared their approach to differential privacy, claiming that their strategy is 40\% more efficient than differential privacy while achieving the same level of safety and efficiency as FL~\cite{wang2022privacy}.

\subsection{COVID-19 Related Studies on FL in Healthcare}
The recent advancements in smart healthcare and \ac{IoMT} technologies can play a crucial role in helping respond quickly to outbreaks and other health-related crises. Similar to other areas of research, \ac{FL} is recently being used in the analysis of the COVID-19 pandemic~\cite{dayan2021federated, xu2020collaborative, raisaro2020scor, vaid2021federated,yang2021federated, dou2021federated}. A comprehensive review of FL for COVID-19 detection is conducted in~\cite{naz2022comprehensive}. Some of the recent applications of FL for COVID-19 and non-COVID-19 scenarios, along with their current limitations and challenges, are described in~\cite{qian2021value}. Specifically, how the FL could provide valuable care during and post-COVID-19 was a question of interest. Realizing the lack of generalization in AI models, which prevents them from being used in clinical settings, an FL-based unified computed tomography (CT)-COVID AI diagnostic initiative (UCADI) has been proposed in~\cite{xu2020collaborative,bai2021advancing}. The UCADI platform enables users worldwide to utilize and contribute to the model, resulting in a real-world, globally constructed and validated clinic CT-COVID AI application. Alternatively, the authors in~\cite{vaid2021federated,vaid2020federated} utilized FL to predict mortality in hospitalized COVID-19 patients. Patient information was gathered from the EHR of five different hospitals within the Mount Sinai Health System (MSHS). The authors demonstrated that FL models outperformed locally trained models to predict mortality and promise to develop effective predictive models without jeopardizing patient privacy. In~\cite{dayan2021federated}, an FL model called EXAM (electronic medical record (EMR) chest X-ray AI model) was trained using data from 20 institutes around the world to estimate and predict the future oxygen requirements of symptomatic COVID-19 patients using vital signs, laboratory data, and chest X-rays. The authors in~\cite{feki2021federated} introduced a collaborative FL framework that allows multiple medical institutions to use \ac{DL} to screen COVID-19 from chest X-ray images without sharing/exchanging the patient data. Similarly, there have been several studies that propose the use of FL for COVID-19 X-ray data training and deploy experiments to verify the FL  effectiveness~\cite{yan2021experiments,liu2020experiments,zhang2021dynamic,abdul2021covid,bhattacharya2022application,ho2021dpcovid,de2022fully,cetinkaya2021communication,ulhaq2020covid,banerjee2020multi,alam2021federated,slazyk2022cxr,cao2021near,boyi2020experiments}.\\
\indent A hybrid framework for COVID-19 prediction via federated \ac{ML} models is investigated in~\cite{kallel2022hybrid}. An asynchronously updating FL model for mobile and deployable resource nodes to create local AI models for COVID-19 detection without explicit radiograph data exchange with the cloud is proposed in~\cite{sakib2021covid}. In~\cite{qayyum2021collaborative}, Qayyum et al. utilized the emerging concept of clustered FL (CFL) for an automatic diagnosis of COVID-19  at the edge by training a multi-modal ML model capable of detecting COVID-19 in both X-ray and Ultrasound imagery. The authors also envisaged that CFL is found to cope better with the divergence in data distribution from different sources than the conventional FL technique. Similarly, in \cite{chen2021multi}, Chen et al. discussed the multi-modal COVID-19 discovery based on collaborative FL. In~\cite{jaladanki2021development}, FL was used to predict acute kidney injury within three and seven days of admission in 4029 adults hospitalized with COVID-19 at five socio-demographically diverse New York City hospitals using demographics, comorbidities, vital signs, and laboratory values. The prediction performance of FL models was often found higher than that of single-hospital models and comparable to that of pooled-data models. \\
\indent A 5G-enabled architecture of auxiliary COVID-19 diagnosis based on FL for various institutions and central cloud cooperation was proposed by Wang et al. in order to realize the sharing of diagnosis models with high generalization performance~\cite{wang2021auxiliary}. A COVID-19 diagnosis model cognition framework was built on sharing and updating the model adaptively between the central and distributed nodes to interchange models and parameters. Federated \ac{DL} for detecting COVID-19 lung abnormalities in CT through a multinational validation study was conducted in \cite{dou2021federated}. The authors investigated FL strategies for developing an AI model for COVID-19 medical image diagnostics with good generalization capabilities on unseen multinational datasets. A secure international informatics FL infrastructure to investigate COVID-19 is initiated in \cite{raisaro2020scor}. To address the privacy dilemma for COVID-19 data, the SCOR consortium is formed that has created a ready-to-deploy secure FL infrastructure based on privacy and security technology. For COVID-19 impacted region segmentation in 3D chest CT, the authors in \cite{yang2021federated} presented a federated semi-supervised learning architecture using multinational COVID-19 data from China, Italy, \& Japan. The proposed approach can extract useful information from clients who only have unlabeled data.

\subsection{Activity Recognition for Enhanced Elderly Care Related Studies on FL in Healthcare}

Human activities can be of different kinds, for example: sitting, standing, sleeping, walking, running, etc. Activity recognition by computer vision is a field that has the potential to significantly impact elderly care by enabling not just preventive and predictive actions but also individualized care via intelligent fall detection solutions~\cite{brenvcivc2020intuitive}. FL is an interesting domain for the activity recognition field that can provide enhanced elderly care solutions for aging populations worldwide while preserving their privacy. In \cite{sozinov2018human}, Sozinov et al. compared the performance of FL to that of centralized learning by developing a deep neural network and a softmax regression model trained on both synthetic and real-world datasets. They also investigated the transmission cost as well as the impact of erroneous clients with corrupted data in an FL context. Their experiments showed that FL could provide models with somewhat lower but acceptable accuracy than centralized ML models for the human activity recognition task. Accuracy of up to 89\% in the FL setting was achieved compared to 93\% accuracy in the centralized model. To develop an efficient feature representation for activity recognition, the authors in~\cite{chen2019federated} presented a novel federated multi-task hierarchical attention mode (FATHOM) that jointly trained classification/regression models from multiple sensors. In~\cite{bramfederated}, a federated \ac{ML} software architecture was introduced that can train a number of deep neural network types with large amounts of unlabeled data that are often present at distributed clients in an FL setup. FL for the aging population suffering from Alzheimer's/dementia disease is explored in~\cite{hesseberg2020federated}.

\subsection{FL in Medical Image Analysis}
\label{FL_medical_image_analysis}

\begin{figure*} [!t]
    \centering
    \includegraphics[trim=2cm 0.5cm 2.5cm 1cm, clip, width=0.6\linewidth]{images/pie1.png}
    \caption{Estimated number of cancer incidence worldwide in 2020 (including both sexes and all ages). Breast cancer remains the leading cause of cancer followed bylung, colorectum, prostate, stomach, liver, and esophagus cancer (GLOBOCAN 2020 statistics)~\cite{sung2021global}.}
    \label{fig:cancerstatistics1}
\end{figure*}

\begin{figure*}[!t]
    \centering
    \includegraphics[trim=2cm 0.5cm 2.5cm 1cm, clip, width=0.6\linewidth]{images/pie2.png}
    \caption{Estimated number of cancer mortality in 2020 worldwide, including both sexes and all ages. Lung cancer remained the leading cause of cancer-related mortality, followed by colorectum, liver, stomach, breast, oesophagus, pancreas, and other cancer (GLOBOCAN 2020 statistics)~\cite{sung2021global}.}
    \label{fig:cancerstatistics2}
\end{figure*}

\ac{FL} is being increasingly explored in medical image analysis to train the \ac{DL} models on the large datasets distributed across multi-center and cross-border~\cite{roth2020federated, sheller2020federated, remedios2020federated}. It allows each medical center to train a global model collaboratively in FL settings. Cross-domain collaborative and decentralized learning has several advantages. In the field of medical imaging, data annotation is one of the crucial and labor-intensive tasks. Using \ac{FL}, different institutions can benefit from each other's annotations without even sharing them. Training \ac{DL} algorithms require high computational power and memory space. Using \ac{FL} can help in efficient training and memory consumption for AI-assisted medical image analysis algorithms.

In the next sub-sections, we will briefly outline the existing works on different types of medical use cases and their application using \ac{FL}. In this regard, we will cover the examples of the most commonly occurring cancer in terms of incidence and the most deadly cancer in terms of mortality. We also discuss the application of \ac{FL} in developing the computer-aided diagnosis tool for those medical cases. The most frequently occurring cancer in terms of incidence are breast, lung, colorectum, prostate, stomach, liver, etc. (please refer to Fig.~\ref{fig:cancerstatistics1}). The most lethal cancers are lung cancer, colorectum cancer, liver cancer, stomach cancer, breast cancer, esophagus cancer, pancreas, etc.~\cite{sung2021global} (see Fig.~\ref{fig:cancerstatistics2}). Below, we briefly describe the most commonly caused cancer both in terms of incidence and mortality.

\vspace{1.0ex}
\noindent \textbf{Breast cancer segmentation}.
Breast cancer is the first leading cause of cancer and the third leading cause of mortality. The Global Cancer Observatory (GLOBOCAN) accounts for 11.7\% of the incidence and 6.9\% of the mortality~\cite{sung2021global}. Roth et al.~\cite{roth2020federated} used FL to train a \ac{DL}-based model for breast density classification using a collaborative approach with seven clinical institutions worldwide. Their experimental results showed that the model trained under an FL setting outperformed the individually trained model on each institute's local data by 6.3\% on average. Additionally, a relative improvement of 45.8\% was observed in the model's generalizability when the FL-based model was evaluated on the external testing datasets from other participating sites. These improvements are explained by the larger training datasets obtained from seven different institutions under FL settings without requiring to share any data and respecting privacy. Similarly, Sanchez et al.~\cite{jimenez2021memory} defined a memory-aware curriculum learning method
for the FL settings. Their curriculum controlled the order of the training samples and paid special attention to forgotten samples after the deployment of the global model. Their approach combined the unsupervised domain adaptation to deal with the domain shift problem while preserving data privacy. They evaluated their method on three clinical datasets from different centers. Their results showed that federated adversarial learning was advantageous for the multi-site breast cancer classification.

\vspace{1.0ex}
\noindent \textbf{Lung cancer detection}
Lung cancer is the second leading cause of cancer in terms of incidence and the leading cause of cancer-related death worldwide. It accounts for 11.4\% of all cancer-related incidence and accounts for 18\% of global cancer-related death~\cite{sung2021global}. Adnan et al.~\cite{adnan2022federated} studied the effects of IID and non-IID distributions along with the healthcare providers by conducting a case study. They proposed a deferentially private FL framework as the potential solution to analyze histopathology images. For the experimentation, they used the publicly available Cancer Genome Atlas (TCGA) dataset for simulating a distributed environment and empirically compared the performance of private, distributed training to conventional training. Their work revealed deferentially private learning as a potential framework for the collaborative and decentralized development of \ac{DL} models in medical image analysis tasks.

\vspace{1.0ex}
\noindent \textbf{Colorectal polyp segmentation}.
Colorectal cancer is the third leading cause of cancer-related incidence and the second leading cause of death~\cite{sung2021global}. Colonoscopy is the gold standard for the early detection of cancer. However, there is a high adenoma miss-rate of 20-24\%~\cite{leufkens2012factors}. Moreover, the examination procedure is expensive and resource-demanding. A computer-aided diagnosis system can help detect neoplastic polyps during screening and aid in the early detection of cancer. FL can be one of the potential solutions to aid the development of computer-aided diagnosis systems for colonoscopy screening. To the best of our knowledge, there is no collaboration on developing decentralized artificial intelligence in colorectal polyp segmentation. The reason for this is that the availability of a diverse and multi-center dataset remains one of the major challenges.

To address the challenge of the lack of a multi-center dataset in the colonoscopy domain,  Ali et al.~\cite{ali2021polypgen} have curated and publicly released the PolypGen dataset, a six-center dataset that is suitable for exploring \ac{FL}. PolypGen consists of 3762 polyps frames from more than 300 patients dataset. It consists of datasets from six unique clinical institutions (Norway, France, Italy, Egypt, and the UK). Each center dataset is stored in a separate folder. Thus, this dataset can be advantageous for training multiple \ac{DL}-based models independently on the colonoscopy datasets on separate computers and sharing the learned model weight. Although there has been work done on PolypGen dataset~\cite{ali2022assessing}, training the model on the FL-based paradigm still remains an open research area.

\vspace{1.0ex}
\noindent \textbf{Prostate cancer segmentation}. Prostate cancer is the second leading cause of cancer-related death (after lung cancer) in the American male population~\cite{aldoj2020semi}. An accurate and automatic prostate segmentation can play an important role in facilitating diagnostic and therapeutic applications such as early detection of cancer, patient management, treatment planning, and surgical planning~\cite{wang2015towards}. Manual segmentation is the commonly used method for the segmentation of prostate and prostate regions despite being a time-consuming and subjective process. In this regard, Sarma et al.~\cite{sarma2021federated} performed a multicenter study on the whole prostate segmentation on the axial T2- weighted \ac{MRI} scans. They used 3D Hybrid Anisotropic Hybrid Network~\cite{liu20183d} model to train on each institution's data. Additionally, a  single model was trained using \ac{FL} across all three institutions. \ac{FL} based model had better performance and generalizability than the model trained on single institutions. Xia et al.~\cite{xia2021auto} proposed addressing the federated domain generalization problem. They proposed Federated Domain Generalization (FedG), a generalizable model that aims to learn a federated model from multiple distributed source generalizations that suffered from performance drops on unseen data outside of the federation. They trained FedG on a six prostate dataset. The experimental results showed superior efficacy of FedG over the state-of-the-art methods.

\vspace{1.0ex}
\noindent \textbf{Small bowel disease detection}.
Capsule endoscopy is the procedure to examine the small intestine disease. The physicians recommend a capsule endoscopy procedure for identification of gastrointestinal bleeding, diagnosing inflammatory disease (Crohn's disease), cancer, small intestine polyp, etc. The development of a \ac{DL}-based computer-aided diagnosis system can improve the anomaly detection rate in the small intestine and reduce manual labor. However, the capsule endoscopy dataset is scarce, and no multi-center dataset is publicly available for experimentation.  One of the large-scale datasets in this field is KvasirCapsule dataset~\cite{smedsrud2021kvasir} that contains  4,741,504 image frames.  This dataset was collected at Baerum hospital at Vestre Viken (VV) Hospital Trust in Norway. VV consists of four hospitals; therefore, this dataset can be treated as a multi-center dataset. However, the dataset from each center is not provided in a separate folder or no information is provided about the labeled and unlabelled videos about at which hospital they were collected. To the best of our knowledge, there is no work on video capsule endoscopy for small bowel disease detection or gastrointestinal tract bleeding detection using \ac{FL}. Both exploring the dataset in the \ac{FL} setting and the use of \ac{FL} for small bowel disease detection remain open issues.

\vspace{1.0ex}
\noindent \textbf{Liver segmentation}.
Liver disease is of high importance because of its high incidence. It is the third leading cause of mortality in both men and women~\cite{sung2021global} (see Fig.~\ref{fig:cancerstatistics2}). An early diagnosis of liver disease can play an important role in treating and preventing damage. Liver segmentation is the most commonly adopted technique for diagnosis and follow-up treatment. \ac{FL}-based solutions recently gained popularity by training the shared global models by distributed clients with heterogeneous image datasets. Bernecker et al.~\cite{bernecker2022fednorm} proposed two \ac{FL} algorithms, namely FedNorm and FedNorm+, that were based on modality-based normalization techniques. They validated their method on the multi-modal and multi-institutional datasets (6 centers, 428 patients). They obtained the high performance of dice coefficients up to 0.9610 and consistently outperformed locally trained models that were trained in each center. In some cases, the federated models also even surpassed centralized models. Similarly, recently Xu et al.~\cite{xu2022federated} presented a new federated multi-organ segmentation U-Net for the segmentation of multiple organs. Here, the multi-encoding U-Net (MENU-Net) extracts organ-specific features via encoding sub-networks. Out of the different sub-networks, each of the sub-network is considered an expert of a specific organ and is trained for that client. They have regularized the training of the MENU-Net by designing an auxiliary generic decoder. This was performed to encourage organ-specific feature enhancement (for example, becoming distinctive and informative). They have trained their method on four abdominal CT image datasets (liver, kidney, pancreas, and BTCV). The extensive experimental results on four different datasets showed the potential of the proposed method in solving the partial level problem in \ac{FL} context.

\vspace{1.0ex}
\noindent \textbf{Brain tumor segmentation}.
Brain tumor segmentation is an important process for separating tumors from normal tissue. In the clinical setting, it can provide key details for each part of the tumor that is useful for diagnosis and treatment planning. \ac{MRI} has been extensively used by medical professionals for diagnosing brain tissue~\cite{bakas2017advancing}. However, a manual process is time-consuming and can only be accomplished by professional neuroradiologists~\cite{tang2018multi}. An automatic brain tumor segmentation can significantly impact brain tumor diagnosis and treatment. Li et al.~\cite{li2019privacy} implemented the feasibility of using the differential-privacy technique to protect the patient data in the \ac{FL} setup. As a use case, they considered the practical learning systems for brain tumor segmentation on the BraTS 2018 dataset~\cite{bakas2017advancing,menze2014multimodal}. For the evaluation, they compared federated vs. centralized data training. Their decentralized model converged at 300 training epochs, whereas the \ac{FL} converged at about 600 epochs. The data-centralized training took about 205.70s per epoch, whereas 65.45s plus small overheads.

Similarly, Sheller et al.~\cite{sheller2018multi} performed a multi-institutional study where they studied the feasibility of brain tumor segmentation. Their federated semantic segmentation model achieved a dice coefficient of 0.8520 on multimodel brain scans and achieved a dice coefficient of 0.8620 with the model trained by sharing the dataset. Due to the lack of a centralized public dataset, they used 2018 BraTs data for training their \ac{FL} based effective segmentation model by iteratively aggregating the locally trained UNet segmentation models at a centralized server. They compared the performance of \ac{FL} models with traditional institutional incremental learning (IIL) and cyclic institutional incremental learning (CILL) and showed improvement over both the techniques.

\vspace{1.0ex}
\noindent \textbf{Pancreas segmentation}.
Pancreas cancer is among the most lethal malignant tumors characterized by high mortality~\cite{hidalgo2010pancreatic}. The GLOBOCAN 2020 statistic shows that it is the seventh leading lethal cancer (see Fig.~\ref{fig:cancerstatistics2}). The five-year survival rate of the patient with the disease is less than nine years~\cite{rawla2019epidemiology}. Therefore, accurate segmentation of pancreatic cancer can play an important role in clinical diagnosis and treatment. In this respect, Shen et al.~\cite{shen2021multi} investigated heterogeneous optimization methods with abdominal CT images from the pancreas and pancreatic tumors in \ac{FL} based settings. They utilized three publicly available annotated pancreas segmentation datasets (one consisting of pancreas and tumor, whereas the other consists of healthy pancreas cases) to model three different heterogeneous clients during \ac{FL} settings. They evaluated each method's performance in the \ac{FL} settings and compared their proposed global model's performance with \emph{FedAvg} and \emph{FedProx}. Their experimental results revealed that global models were better than local models for all datasets. Similarly, Wang et al.~\cite{wang2020automated} trained the \ac{FL} models for the segmentation of the pancreas without data sharing. Their dataset (abdominal CT images) consisted of healthy and unhealthy pancreas collected from two different institutions in Taiwan and Japan.  The model utilized for segmentation was coarse-to-fine network architecture search~\cite{yu2020c2fnas} along with variational auto-encoder~\cite{myronenko20183d} at the encoder endpoint. The quantitative results comparison between \ac{FL} settings and locally trained algorithms showed that \ac{FL} framework was better in dealing with unbalanced data distributions and successfully delivered better generalizable models compared to that of standalone training.

\section{Open-Source FL Software Frameworks}
\label{open_source_FL_Software}

\begin{figure} [!t]
\centering
\includegraphics[width=\columnwidth,scale=0.99,keepaspectratio]{images/open_source.png}
\caption{List of open-source \ac{FL} software frameworks.}
\label {FL_Software}
\end{figure}
In this section, we describe the existing FL software frameworks that are available for rapid experimentation and validation of new FL algorithms. There exist some propriety-based FL software frameworks such as IBM FL~\cite{ibmfl2020ibm}, NVIDIA CLARA~\cite{clara}, Substra~\cite{galtier2019substra} and Sherpa.AI FL framework~\cite{rodriguez2020federated} which are not properly documented and lack details that make it challenging to implement and test new FL algorithms. Therefore, we will only focus on the available open-source FL software frameworks as shown in Fig.~\ref{FL_Software} and discuss each one of them in the following sub-sections. A summary of the comparison of different open-source software frameworks is presented in Table~\ref{table:federated_frameworks}.

\subsection{Federated AI Technology Enabler Framework (FATE)}
FATE is an open-source project led by Webank that aims to provide a secure computing framework to serve the federated AI ecosystem~\cite{fate_2019}. FATE allows for the implementation of multiple secure computation protocols to enable big data collaboration complying with data protection regulations. However, its script-based high-level interface relies on command-line options and a poorly specified domain-specific language.

\begin{table*}
\centering
\caption{Comparison of Open-Source FL Software Frameworks}
\label{table:federated_frameworks}
\begin{tabular}{l|c|c|c|c|c|c|c|c|c}
\hline
\textbf{Features}                    & \textbf{FATE} & \textbf{TFF}                                        & \textbf{OpenFL} & \textbf{Fed-BioMed} & \textbf{PySyft}                                     & \textbf{FedML}                                      & \textbf{LEAF} & \textbf{PaddleFL} & \textbf{PrivacyFL} \\ \midrule
FL model with different datasets     & Yes           & Yes                                                 & Yes             & Yes                 & Yes                                                 & Yes                                                 & Yes           & Yes               & Yes                \\ \midrule
FL attack simulator                  & No            & No                                                  & No              & No                  & No                                                  & No                                                  & No            & No                & Yes                \\ \midrule
Documentation and tutorials details   & Partial       & Partial                                             & Yes             & Partial             & Yes                                                 & Partial                                             & Partial       & No                & No                 \\ \midrule
Other library support                & No            & No                                                  & Yes             & Partial             & Yes                                                 & No                                                  & Yes           & No                & No                 \\ \midrule
Data Partitioning                    & Yes           & Partial                                             & Yes             & Partial             & Yes                                                 & Yes                                                 & Partial       & Yes               & Partial            \\ \midrule
Hardware support                     & CPUs          & \begin{tabular}[c]{@{}c@{}}GPUs\\ CPUs\end{tabular} & CPUs            & CPUs                & \begin{tabular}[c]{@{}c@{}}GPUs\\ CPUs\end{tabular} & \begin{tabular}[c]{@{}c@{}}GPUs\\ CPUs\end{tabular} & CPUs          & CPUs              & CPUs               \\ \midrule
FL specific for medical applications & No            & No                                                  & Yes             & Yes                 & No                                                  & No                                                  & No            & No                & No                 \\ \midrule
\end{tabular}
\end{table*}

\subsection{Tensor Flow Federated (TFF)}
TFF is an open-source framework for \ac{ML} on decentralized data~\cite{tff}. TFF allows developers to experiment with novel algorithms and simulate the included FL algorithms on their models and data. TFF interfaces are divided into two layers, namely FL API and Federated Core API. The FL API assists \ac{ML} developers in implementing FL to TF models and FL researchers in introducing new methods, while the Federated Core API assists systems researchers. TFF is backed up by a strong community of developers and has support from Google, which actively uses TFF to support consumer needs. However, TFF lacks differential privacy (DP) mechanisms to implement new privacy-preserving algorithms.

\subsection{Open-\ac{FL} (OpenFL)}
OpenFL is an open-source framework that is being used for training ML algorithms utilizing the FL data-private collaborative learning paradigm~\cite{reina2021openfl}. OpenFL works with TensorFlow and PyTorch training pipelines, and it can be adapted to other ML and \ac{DL} frameworks. OpenFL is designed to serve as the backend for the FeTS (Federated Tumor Segmentation)\footnote{https://www.med.upenn.edu/cbica/fets/} which is a real-world medical FL platform for tumor segmentation. The OpenFL library is made up of two parts: the collaborator, which trains global models using a local dataset, and the aggregator, which receives model updates and aggregates them to produce the global model. OpenFL enables developers to train ML models on different data owners' nodes. The ML model is trained on the hardware at the collaborator node. OpenFL does not provide any DP mechanism nor any DP algorithm.

\subsection{Fed-BioMed}
Fed-BioMed\footnote{https://fedbiomed.gitlabpages.inria.fr/} is an open-source initiative aimed at enabling biomedical research using non-centralized statistical analysis and \ac{ML} methodologies~\cite{silva2020fed}. The project, which is currently based on Python, PyTorch, and Scikit-learn, allows for the development and deployment of FL analysis in real-world \ac{ML} systems. Fed-BioMed is an ongoing initiative, and it still needs to be developed to be scalable and fully deployed in a practical healthcare setting.

\subsection{PySyft}
PySyft is an open-source Python3 library that utilizes FL, differential privacy, and encrypted computations to enable FL for research purposes~\cite{pys}. It was created by the OpenMined\footnote{https://www.openmined.org/} community and mostly works with \ac{DL} frameworks like PyTorch and TensorFlow. PySyft defines objects, \ac{ML} methods, and abstractions. With PySyft, it is not possible as of now to work on ML/AI projects requiring network communication. This would necessitate the use of another package known as PyGrid\footnote{https://blog.openmined.org/what-is-pygrid-demo/}. Moreover, it does not provide any DP mechanism nor any DP algorithm.

\subsection{FedML}
FedML is an open-source research framework and benchmark designed to make FL algorithm development and performance comparison easier~\cite{he2020fedml}. FedML offers computing paradigms such as on-device training for edge devices, distributed computing, and single-machine simulation. It includes a lightweight, cross-platform Edge AI SDK that can be deployed on edge GPUs, smartphones, and IoT devices. However, FedML is not properly documented, and it does not provide DP algorithms.

\subsection{LEAF}
LEAF is an FL benchmarking framework with applications such as FL, multi-task learning, meta-learning, and on-device learning~\cite{caldas2018leaf}. It is made up of a collection of open-source datasets, an array of statistics and system metrics, and a set of reference implementations that enables researchers to think about new proposed solutions while making more reasonable assumptions than previous benchmarks. However, LEAF does not offer as much official documentation, and DP benchmarks are not provided.

\subsection{PaddleFL}
PaddleFL is a PaddlePaddle-based open-source FL framework~\cite{paddle}. It is basically a parallel distributed \ac{DL} framework suitable for industrial practice. PaddleFL allows researchers to reproduce and compare various FL techniques and algorithms rapidly. PaddleFL also benefits developers by making it simple to implement an FL system in large-scale distributed clusters. It provides a differentially private stochastic gradient descent implementation when it comes to DP. One of its key disadvantages is that it is poorly documented and lacks any tools for quickly extending its capabilities.

\subsection{PrivacyFL}
PrivacyFL is an FL open-source framework that is extendable, configurable, and scalable~\cite{mugunthan2020privacyfl}. Its primary features include latency simulation, client departure/failure resilience, centralized and decentralized (serverless) learning support, and configurable privacy and security measures based on differential privacy and safe multiparty computation (MPC). Users can use PrivacyFL to determine whether it is advantageous for them to participate in differentially-private FL for a given differentially private algorithm. PrivacyFL is a relatively new FL framework and lacks detailed documentation for extending and implementing new FL algorithms.

\section{Challenges of \ac{FL}}
\label{opportunities_and_challenges_FL}

When it comes to the deployment of FL in real-world healthcare settings, there are multiple challenges associated with FL. We have divided these challenges of FL into four main categories. The main challenges of FL are communication, statistical, compute, security, and privacy challenges. We will describe each of these challenges in the next subsections.

\subsection{Communication Challenges}
In FL, communication is an important paradigm since the participating nodes or clients need to communicate their FL results in each round to the centralized server in a centralized FL system, or they need to update/train their global learning parameters in a distributed FL system. In an FL system, multiple downlinks and uplink communication rounds exist between a server and participating clients to train a global model that requires considerable communication bandwidth~\cite{zheng2020design}. Hence, communication bandwidth can be a bottleneck for the FL environment when a large number of nodes or clients are participating in the FL process. Considering multiple healthcare services providers that could be located at geographically different places might have unreliable or unstable network connections hindering the participating healthcare facilities in the FL process to train a global ML model.

\subsection{Statistical Challenges}
The statistical challenge, i.e., data distribution among the available clients in the network participating in the FL process, is one of the main challenges of FL. EHR is considered one of the prominent sources of healthcare data for FL applications. A detailed survey of existing works on EHR data for FL applications is outlined in~\cite{dang2022federated}. Training an ML/AI algorithm on EHR data from a single hospital might introduce some amount of bias and may not be generalizable. Moreover, small health clinics in rural areas might only have limited EHR data in medical settings. Thus, an individual health clinic dataset in an FL  environment may not be representative of the population distribution of the other health clinic participants and their datasets. In addition, these clinics may not have the advanced infrastructure to label all medical/health-related data. Therefore, unlabelled health data from these rural clinics could grow exponentially, especially in the developing world, where small health facilities are provided by the government with limited infrastructural support. All of these problems lead to significantly different data points and labeled/unlabeled data when small health clinics in rural areas and hospitals in metro cities participate in the FL process to train a global ML model. This data distribution paradigm defies common identical and independently distributed (I.I.D.) assumptions in distributed FL optimization, increases the possibility of stragglers, and may add modeling, analysis, and evaluation complexity for FL algorithms~\cite{ding2022interval}.

\subsection{Computation Challenges}
It has been predicted that more than 80\% generated data by the edge devices will be processed at the edge of the network itself by 2030~\cite{peltonen20206g}. Thus, there is an increased interest in using Edge-AI either on the edge devices or at the edge of the network. The computation capabilities, storage capacity, and energy consumption of different devices in the network might differ due to the variability in their hardware components. However, due to network capacity and system-related capability on each device, only a small fraction of the devices are typically active at any given time in an FL process. For example, just a few hundred devices out of millions may be active at any given time. Each device may be unreliable, affecting its data quality, and it can be quite usual for an active device to stop working considering energy constraints during an FL iteration process. In the cross-device FL setting in healthcare, the healthcare devices such as smartwatches and health gadgets have limited energy and do not have enough computation and storage capabilities to train complex ML/AI algorithms~\cite{lo2022architectural}.\\
\indent Moreover, due to the energy constraint, they cannot participate in the FL iteration process involving multiple communication rounds with the global server. Thus, in cross-device FL settings in healthcare, each on-device AI model needs to be small and lightweight. On the other hand, cross-silo FL is a more popular architecture in healthcare settings where multiple hospitals or similar organizations can participate in an FL process to train a global model~\cite{diddee2020crosspriv,nguyen2022novel}. However, data disparity among multiple hospitals' data may lead to a higher number of FL iterations to reach the desired accuracy on a global server, thereby increasing the computation time.

\subsection{Security and Privacy Challenges}
Here, we will discuss the security and privacy-specific challenges of FL systems. As shown in Fig.~\ref{Federated_Learning_Architecture}, without having direct access to the client's local data, FL systems allow distributed clients to collaborate and train \ac{ML} and \ac{DL} models by sharing training parameters. However, instead of centralizing raw data, sharing gradient updates to a central server trained on the local clients could lead to reverse engineering attacks by passively intercepting the gradients exchanged during the training process~\cite{fredrikson2015model, fredrikson2014privacy}. This means malicious clients can introduce a backdoor functionality that compromises the underlying FL system during the training process of the global federated model~\cite{bagdasaryan2020backdoor} which can be against the GDPR since medical data are highly sensitive and private data. An attacker can use a backdoor functionality to mislabel specific jobs without affecting the overall accuracy of the global model. One possible option to address such a critical issue is to encrypt the global gradients shared in the distributed network. Differential privacy at the client level can help protect against backdoor attacks, but it comes at the cost of significantly reducing the performance of the global model~\cite{geyer2017differentially}. Other existing technical approaches also prevent a passive attacker from violating the privacy of data and information leakage by exploiting the global model outputs using malicious model updates. For example, the work in~\cite{jayaram2020mystiko} presents a novel cryptographic key generation and sharing approach that leverages additive homomorphic encryption to maximize the confidentiality of federated gradient descent in the training of deep neural networks without any loss of accuracy.

The work in~\cite{mothukuri2021survey} identifies and examines some of the security vulnerabilities and threats in FL systems and provides insights into the existing defense techniques and future approaches for improving the security and privacy of FL implementations.

\section{Open Problems and Future Research Directions of FL in Medical Context}
\label{Open_Problems_FL}

As outlined in Section~\ref{FL-Context-of-Healthcare}, lots of different technologies have emerged that can be used in conjugation with FL, especially for medical applications. FL is still in its infancy, and lots of research needs to be done before having a practical framework for FL in medical settings. There is still a large research gap and challenging open problems that need to be addressed. Based on the main challenges identified in the previous section, we will explain some of the open problems and future research directions of FL in a medical context.

\subsection{\textbf{Hyperparameter Optimization for Efficient FL Systems}}
One of the open problems in the FL research is hyperparameter optimization. Even before starting an FL process, an AI model with properly optimized hyperparameters need to be designed that can be communicated from the FL server to multiple clients to train on their private medical data. This hyperparameter optimization is difficult to achieve on a federated medical dataset since the clients do not want to participate and contribute before benefiting from the FL algorithm through their involvement in the system. One of the possible ways to achieve this is to design an appropriate auction mechanism with incentives that would motivate the clients to contribute toward hyperparameter optimization~\cite{deng2021auction}.

\subsection{\textbf{Security and Privacy-enabled FL Systems}}
\indent Since several federated clients are involved in the FL process, it can be highly susceptible to security attacks such as model poisoning attacks~\cite{bhagoji2019analyzing}. A malicious client can misclassify their input and influence the global model on the server. This situation is highly undesirable in a medical environment. Although some of the work in~\cite{malekzadeh2021dopamine} have introduced methods to distinguish between benign and malicious models. They have their own limitations as such methods can only be applied in controlled settings under specific environments. Therefore, this is an open and challenging research issue that needs to be solved before deploying FL in a practical medical environment.

\subsection{\textbf{Efficient Communication Paradigm for FL Systems}}
\indent As pointed out earlier, when a large number of clients, such as smartwatches and health gadgets, are available in the FL network, communication can be a bottleneck because they must download the model from the server during the downlink communication. The FL clients must update their model parameters after training on their private dataset to the server during the uplink communication. One of the research problems is designing a lightweight AI model that could be downloaded from the server without sacrificing the model's accuracy. Sparsification, subsampling, and quantizations are some of the model compression strategies that help reduce the message sizes that are conveyed at each update round~\cite{paragliola2022definition}. Another exciting research direction is to reduce the uplink communication rounds between the FL clients and the server, optimize the communication bandwidth, reduce the convergence time of the FL algorithm, and save the FL clients' energy in the network.

\subsection{\textbf{Solving Medical Data Heterogeneity and Statistical Issues of FL Systems}}
\indent Because of FL's distributed nature, data distributions across medical institutions are frequently heterogeneous. Several research studies have pointed out that the FL performance degrades with the increasing degrees of data heterogeneity. Thus, another open problem to solve in FL is its medical data heterogeneity. FL can combine medical data, but combining horizontal medical data from medical institutions across regions and longitudinal medical data from the same patient across hospitals remains one of the most difficult challenges in FL. Although there are a few works such as~\cite{qu2021experimental,sheller2020federated} that have investigated the medical data heterogeneity problem across multiple institutions, more research efforts are required for the results to be generalizable.

\subsection{\textbf{Designing Intelligent Incentive Algorithms for FL System}}
\indent In FL, the federated medical clients may not want to participate in the FL process and provide their data to train a global model owing to their cost of communication, energy, latency, etc.~\cite{yu2020fairness}. This will lead to a model that may not be well suited for medical applications. In order to have a more generalizable FL model, we need FL clients for their honest participation with good contribution from their high-quality data. Therefore, an incentive mechanism needs to be designed to motivate clients and reward them for their contributions with high-quality data. Also, more research is needed in designing an intelligent FL algorithm that will discard clients with bad quality data and restrict their participation in the FL process~\cite{kang2019incentive}.

\subsection{\textbf{Contrastive Learning as a Solution for Un-labelled Medical Data Distribution}}
\indent Most of the research works in FL are concentrated on the supervised learning models where the datasets arelabeled~\cite{ji2021emerging,fallah2020personalized}. However, unlabelled datasets could grow in numbers when small health organizations and big hospitals participate in the FL for training a global model. To overcome this unlabelled datasets problem, semi-supervised and unsupervised learning have recently been used in FL by some researchers~\cite{kassem2022federated,itahara2020distillation,gornitz2014learning,zhu2021federated}. Although semi-supervised and unsupervised learning can solve the problem related to the unlabelled datasets, they often fail to achieve high performance in medical image datasets with \ac{DL} models. In this regard, contrastive learning has recently become a popular research field for learning unlabeled data representations by training an FL model on unlabeled data~\cite{li2021model}. Contrastive learning, also known as self-learning, is a pre-training procedure in which the model attempts to learn similar and distinct data samples from an unlabeled data distribution~\cite{he2020momentum,chen2020simple}. Thus, contrastive learning seems very relevant for applying FL in the medical domain as it can be used on unlabelled and non-IID medical data. This is an interesting research direction, and more research is needed to be explored in FL settings.

\subsection{\textbf{Benchmarking FL}}
\indent FL is a new and hot research topic with immense potential in the medical field. Another interesting research direction for the researchers in academia and industry is to expand on existing implementations and benchmarking tools as outlined in Section IV to make empirical results more reproducible and new FL solutions more generalizable and widely available. Also, open FL-based medical datasets should be made available for the research community for benchmarking FL algorithms suitable for medical applications.

\subsection{\textbf{Regulatory Frameworks for FL-enabled Medical Applications}}
Considering the strict regulations such as GDPR on the security and privacy of medical data, a guideline needs to be developed that allows researchers to evaluate actual risks and concerns related to the medical data when using it in the FL framework. So far, we do not have enough knowledge and the possibility to check if privacy regulations of medical data are being violated when using it for FL applications. This calls for joint cooperation between computer science and law community researchers to address the security and privacy regulations of medical data in the FL framework. Moreover, the explainability of FL models is still an open problem that should be addressed by researchers to deploy practical FL models in medical environments~\cite{samek2017explainable,chen2022evfl,wang2019interpret}.

\section{Conclusions}
\label{conclusion}

In privacy-preserving settings where the training of medical data is not directly accessible, FL has emerged as the de facto decentralized learning framework for medical applications. Therefore, FL has been shown to be quite effective in the medical field. However, it faces several challenges, including system heterogeneity, statistical heterogeneity, communication, security, and privacy issues. This article provides an overview of FL and discusses how FL is applied in conjugation with several emerging technologies to tackle some of the challenges in the medical domain. We put up a complete summary of findings and more recent research studies that focus on FL in a medical context. Then we describe FL's specific major challenges in in-depth, available open-source FL frameworks, as well as a critical analysis of open problems and future research directions of FL in a medical context to be explored by the FL research community to design reliable and scalable FL models for data-driven healthcare. Solving these open problems of FL for medical applications will need a collaborative effort from a diverse range of interdisciplinary research communities in health, computer science, and law. \\
\indent In the near future, we expect FL to be practically deployed for medical applications, resulting in more complex protocols with security and privacy assurances, an energy-efficient communication paradigm for FL networks, as well as the actual implementation of FL technology to solve real-world challenges in the healthcare arena. Finally, we hope that this survey paper acts as a checkpoint that sets forth the current state-of-the-art works, open problems, and future research directions of FL, especially for medical applications in detail. We are confident in FL's potential influence on healthcare that can significantly increase efficiency in healthcare systems worldwide.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv