  Existing deep learning technologies generally learn the features of chest
X-ray data generated by Generative Adversarial Networks (GAN) to diagnose
COVID-19 pneumonia. However, the above methods have a critical challenge: data
privacy. GAN will leak the semantic information of the training data which can
be used to reconstruct the training samples by attackers, thereby this method
will leak the privacy of the patient. Furthermore, for this reason that is the
limitation of the training data sample, different hospitals jointly train the
model through data sharing, which will also cause the privacy leakage. To solve
this problem, we adopt the Federated Learning (FL) frame-work which is a new
technique being used to protect the data privacy. Under the FL framework and
Differentially Private thinking, we propose a FederatedDifferentially Private
Generative Adversarial Network (FedDPGAN) to detectCOVID-19 pneumonia for
sustainable smart cities. Specifically, we use DP-GAN to privately generate
diverse patient data in which differential privacy technology is introduced to
make sure the privacy protection of the semantic information of training
dataset. Furthermore, we leverage FL to allow hospitals to collaboratively
train COVID-19 models without sharing the original data. Under Independent and
Identically Distributed (IID) and non-IID settings, The evaluation of the
proposed model is on three types of chest X-ray (CXR) images dataset (COVID-19,
normal, and normal pneumonia). A large number of the truthful reports make the
verification of our model can effectively diagnose COVID-19 without
compromising privacy.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning