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Title	Robust Federated Learning For Privacy Preservation And Efficiency In Edge Computing
ID_Doc	46858
Authors	Zhou H.; Dai H.; Yang G.; Xiang Y.
Year	2025
Published	IEEE Transactions on Services Computing, 18, 3
DOI	http://dx.doi.org/10.1109/TSC.2025.3562359
Abstract	Federated Learning (FL) has emerged as a key enabler of privacy-preserving distributed model training in edge computing environments, crucial for service-oriented applications such as personalized healthcare, smart cities, and intelligent assistants. However, existing privacy-preserving FL methods are susceptible to multiple privacy leakage attacks (MPLA), where adversaries infer sensitive information through repeated gradient updates. This paper proposes a Robust and Communication-Efficient Federated Learning (RCFL) framework designed to enhance privacy protection and communication efficiency in edge-based service environments. RCFL integrates a global privacy-preserving mechanism with an innovative privacy encoding strategy that minimizes privacy risks over multiple data releases while significantly reducing communication overhead. The proposed framework’s theoretical analysis demonstrates its ability to maintain differential privacy across numerous interactions, ensuring robust model convergence and efficiency. Experimental results using MNIST and CIFAR-10 datasets reveal that RCFL can lower the MPLA success rate from 88.56% to 42.57% compared to state-of-the-art methods, while reducing communication costs by over 90%. These findings underscore RCFL’s potential to enhance security, efficiency, and scalability in service-oriented edge computing applications. © 2008-2012 IEEE.
Author Keywords	edge computing; Federated learning; multiple privacy leakage attack; privacy-preserving

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