VPFL:A verifiable privacy-preserving federated learning scheme for edge computing systems  

作　　者：Jiale Zhang Yue Liu Di Wu Shuai Lou Bing Chen Shui Yu 

机构地区：[1]School of Information Engineering,Yangzhou University,Yangzhou,225009,China [2]Water Conservancy and Civil Engineering College,Inner Mongolia Agricultural University,Hohhot,010018,China [3]Deakin Blockchain Innovation Lab,School of Information Technology,Deakin University,Melbourne,VIC,3125,Australia [4]College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing,211106,China [5]School of Computer Science,University of Technology Sydney,Sydney,2007,Australia

出　　处：《Digital Communications and Networks》2023年第4期981-989,共9页数字通信与网络（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.62206238);the Natural Science Foundation of Jiangsu Province(Grant No.BK20220562);the Natural Science Research Project of Universities in Jiangsu Province(No.22KJB520010).

摘　　要：Federated learning for edge computing is a promising solution in the data booming era,which leverages the computation ability of each edge device to train local models and only shares the model gradients to the central server.However,the frequently transmitted local gradients could also leak the participants’private data.To protect the privacy of local training data,lots of cryptographic-based Privacy-Preserving Federated Learning(PPFL)schemes have been proposed.However,due to the constrained resource nature of mobile devices and complex cryptographic operations,traditional PPFL schemes fail to provide efficient data confidentiality and lightweight integrity verification simultaneously.To tackle this problem,we propose a Verifiable Privacypreserving Federated Learning scheme(VPFL)for edge computing systems to prevent local gradients from leaking over the transmission stage.Firstly,we combine the Distributed Selective Stochastic Gradient Descent(DSSGD)method with Paillier homomorphic cryptosystem to achieve the distributed encryption functionality,so as to reduce the computation cost of the complex cryptosystem.Secondly,we further present an online/offline signature method to realize the lightweight gradients integrity verification,where the offline part can be securely outsourced to the edge server.Comprehensive security analysis demonstrates the proposed VPFL can achieve data confidentiality,authentication,and integrity.At last,we evaluate both communication overhead and computation cost of the proposed VPFL scheme,the experimental results have shown VPFL has low computation costs and communication overheads while maintaining high training accuracy.

关 键 词：Federated learning Edge computing PRIVACY-PRESERVING Verifiable aggregation Homomorphic cryptosystem 

分 类 号：TN91[电子电信—通信与信息系统]