Federated Learning for Iot/Edge/Fog Computing Systems
<|reference_start|>Federated Learning for Iot/Edge/Fog Computing Systems: With the help of a new architecture called Edge/Fog (E/F) computing, cloud computing services can now be extended nearer to data generator devices. E/F computing in combination with Deep Learning (DL) is a promisedtechnique that is vastly applied in numerous fields. To train their models, data producers in conventional DL architectures with E/F computing enable them to repeatedly transmit and communicate data with third-party servers, like Edge/Fog or cloud servers. Due to the extensive bandwidth needs, legal issues, and privacy risks, this architecture is frequently impractical. Through a centralized server, the models can be co-trained by FL through distributed clients, including cars, hospitals, and mobile phones, while preserving data localization. As it facilitates group learning and model optimization, FL can therefore be seen as a motivating element in the E/F computing paradigm. Although FL applications in E/F computing environments have been considered in previous studies, FL execution and hurdles in the E/F computing framework have not been thoroughly covered. In order to identify advanced solutions, this chapter will provide a review of the application of FL in E/F computing systems. We think that by doing this chapter, researchers will learn more about how E/F computing and FL enable related concepts and technologies. Some case studies about the implementation of federated learning in E/F computing are being investigated. The open issues and future research directions are introduced.<|reference_end|>
@article{hasan2024federated,
    title={Federated Learning for Iot/Edge/Fog Computing Systems},
    author={Balqees Talal Hasan and Ali Kadhum Idrees},
    journal={In: Jayakrushna Sahoo, Mariya Ouaissa, Akarsh K. Nair(eds)
  Federated Learning Principles, Paradigms, and Applications. Apple Academic
  Press (2024)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13029},
    primaryClass={cs.NI}
}