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

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. As vehicular networks advance, the Internet of Vehicle (IoV) emerges to enable some real-time applications like audio recognition and multimedia collaboration, aiming to enhance people's daily lives [1], [2]. For IoV, vehicles get information from environment and use their local information to train models in order to enhance vehicle service capabilities. Cui Zhang is with the School of Internet of Things Engineering, Wuxi Institute of Technology, Wuxi 214121, China Xiao Xu and Qiong Wu are with the School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China Pingyi Fan is with the Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China Qiang Fan is with Qualcomm, San Jose, CA 95110, USA Jiangzhou Wang is with the School of Engineering, University of Kent, CT2 7NT Canterbury, U.K. (* The corresponding author, email: qiongwu@jiangnan.edu.cn) Accordingly, the cloud will process the information and provide the relevant vehicles with computational results [4].