EPRFL:An Efficient Privacy-Preserving and Robust Federated Learning Scheme for Fog Computing  

作　　者：Ke Zhijie Xie Yong Syed Hamad Shirazi Li Haifeng 

机构地区：[1]School of Computer Technology and Application,Qinghai University,Xining 810016,China [2]School of Computer and Information Science,Qinghai Institute of Technology,Xining 810016,China [3]Guangdong Key Laboratory of Blockchain Security,Guangzhou University,Guangzhou 510006,China [4]Department of Information Technology,Hazara University,Baffa 21110,Pakistan [5]Qinghai Provincial Key Laboratory of Big Data in Finance and Artificial Intelligence Application Technology,Xining 810016,China

出　　处：《China Communications》2025年第4期202-222,共21页中国通信(英文版)

基　　金：supported in part by the National Natural Science Foundation of China(62462053);the Science and Technology Foundation of Qinghai Province(2023-ZJ-731);the Open Project of the Qinghai Provincial Key Laboratory of Restoration Ecology in Cold Area(2023-KF-12);the Open Research Fund of Guangdong Key Laboratory of Blockchain Security,Guangzhou University。

摘　　要：Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple parties.When combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data aggregation.To address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing scenarios.Specifically,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node dropping.Then,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication overhead.To minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive score.Theoretical analysis demonstrates that EPRFL offers robust security and low latency.Extensive experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.

关 键 词：federated learning fog computing internet of things PRIVACY-PRESERVING ROBUSTNESS 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]