Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing  

作　　者：Zhang Cui Xu Xiao Wu Qiong Fan Pingyi Fan Qiang Zhu Huiling Wang Jiangzhou 

机构地区：[1]School of Internet of Things Engineering,Wuxi Institute of Technology,Wuxi 214121,China [2]School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China [3]Department of Electronic Engineering,Beijing National Research Center for Information Science and Technology,Tsinghua University,Beijing 100084,China [4]Qualcomm,San Jose CA 95110,USA [5]School of Engineering,University of Kent,CT27NT Canterbury,U.K

出　　处：《China Communications》2024年第8期1-17,共17页中国通信（英文版）

基　　金：supported in part by the National Natural Science Foundation of China(No.61701197);in part by the National Key Research and Development Program of China(No.2021YFA1000500(4));in part by the 111 Project(No.B23008).

摘　　要：In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle data.However,based on deep reinforcement learning(DRL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL.At the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the system.In this paper,we proposed a vehicle selection scheme based on DRL in VEC.In this scheme,vehicle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.

关 键 词：asynchronous federated learning byzantine attacks vehicle selection vehicular edge computing 

分 类 号：U463.6[机械工程—车辆工程] TP18[交通运输工程—载运工具运用工程]