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Title	Federated Learning For Cyber Physical Systems: A Comprehensive Survey
ID_Doc	26338
Authors	Quan M.K.; Pathirana P.N.; Wijayasundara M.; Setunge S.; Nguyen D.C.; Brinton C.G.; Love D.J.; Poor H.V.
Year	2025
Published	IEEE Communications Surveys and Tutorials
DOI	http://dx.doi.org/10.1109/COMST.2025.3570288
Abstract	The integration of machine learning (ML) in cyber physical systems (CPS) is a complex task due to the challenges that arise in terms of real-time decision making, safety, reliability, device heterogeneity, and data privacy. There are also open research questions that must be addressed in order to fully realize the potential of ML in CPS. Federated learning (FL), a distributed approach to ML, has become increasingly popular in recent years. It allows models to be trained using data from decentralized sources. This approach has been gaining popularity in the CPS field, as it integrates computer, communication, and physical processes. Therefore, the purpose of this work is to provide a comprehensive analysis of the most recent developments of FL-CPS, including the numerous application areas, system topologies, and algorithms developed in recent years. The paper starts by discussing recent advances in both FL and CPS, followed by their integration. Then, the paper compares the application of FL in CPS with its applications in the internet of things (IoT) in further depth to show their connections and distinctions. Furthermore, the article scrutinizes how FL is utilized in critical CPS applications, e.g., intelligent transportation systems, cybersecurity services, smart cities, and smart healthcare solutions. The study also includes critical insights and lessons learned from various FL-CPS implementations. The paper’s concluding section delves into significant concerns and suggests avenues for further research in this fast-paced and dynamic era. © 1998-2012 IEEE.
Author Keywords	Cyber Physical System; Federated Learning; Machine Learning; Privacy Protection

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