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Title	Federated Learning For Green And Sustainable 6G Iiot Applications
ID_Doc	26342
Authors	Quy V.K.; Nguyen D.C.; Van Anh D.; Quy N.M.
Year	2024
Published	Internet of Things (Netherlands), 25
DOI	http://dx.doi.org/10.1016/j.iot.2024.101061
Abstract	The 6th generation mobile network (6G) is expected to be launched in the early 2030s. The architecture of 6G will be the convergence of space, air, ground, and undersea networks. The power and intelligence of 6G based on advanced AI techniques will realize the concept of the Industrial Internet of Things (IIoT). In this study, we conduct a comprehensive survey of 6G IIoT applications based on Federated Learning (FL), starting from introducing recent advances in FL and IIoT systems to discussing how to integrate them. In particular, we highlight the potential of FL for supporting a range of IIoT systems such as smart medical, intelligent transportation, smart cities, unmanned aerial vehicles, and smart industry. The important discussions to drive FL into IIoT applications are emphasized. Finally, we present challenges and open issues for future research to realize green and suitable FL-based IIoT applications. Index Terms: Federated Learning, Artificial Intelligence, Industrial Internet of Things, Smart IoT Applications © 2024 Elsevier B.V.
Author Keywords	Artificial intelligence; Federated Learning; Industrial Internet of Things; Smart IoT Applications

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