A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles  

作　　者：Naiyu Wang Wenti Yang Xiaodong Wang Longfei Wu Zhitao Guan Xiaojiang Du Mohsen Guizani 

机构地区：[1]School of Control and Computer Engineering,North China Electric Power University,China [2]Department of Mathematics&Computer Science,Fayetteville State University,Fayetteville,NC,USA [3]Department of Electrical and Computer Engineering,Stevens Institute of Technology,Hoboken,NJ,07030,USA [4]Department of Computer Science and Engineering,Qatar University,Doha,Qatar

出　　处：《Digital Communications and Networks》2024年第1期126-134,共9页数字通信与网络（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant 61972148.

摘　　要：The application of artificial intelligence technology in Internet of Vehicles(lov)has attracted great research interests with the goal of enabling smart transportation and traffic management.Meanwhile,concerns have been raised over the security and privacy of the tons of traffic and vehicle data.In this regard,Federated Learning(FL)with privacy protection features is considered a highly promising solution.However,in the FL process,the server side may take advantage of its dominant role in model aggregation to steal sensitive information of users,while the client side may also upload malicious data to compromise the training of the global model.Most existing privacy-preserving FL schemes in IoV fail to deal with threats from both of these two sides at the same time.In this paper,we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL,which uses blockchain as the underlying distributed framework of FL.We improve the Multi-Krum technology and combine it with the homomorphic encryption to achieve ciphertext-level model aggregation and model filtering,which can enable the verifiability of the local models while achieving privacy-preservation.Additionally,we develop a reputation-based incentive mechanism to encourage users in IoV to actively participate in the federated learning and to practice honesty.The security analysis and performance evaluations are conducted to show that the proposed scheme can meet the security requirements and improve the performance of the FL model.

关 键 词：Federated learning Blockchain Privacy-preservation Homomorphic encryption Internetof vehicles 

分 类 号：U495[交通运输工程—交通运输规划与管理] TN929.5[交通运输工程—道路与铁道工程] TP309[电子电信—通信与信息系统]