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.

翻译：在车辆边缘计算（VEC）中，采用异步联邦学习（AFL）技术，边缘端接收局部模型并更新全局模型，有效降低了全局聚合延迟。由于车辆本地数据量、计算能力及位置存在差异，以相同权重更新全局模型并不合适。上述因素会影响局部模型的计算时间与上传时间，同时车辆可能遭受拜占庭攻击，导致车辆数据质量下降。然而，基于深度强化学习（DRL），我们可以综合考量这些因素，尽可能剔除性能较差的车辆，并在异步联邦学习之前排除遭受拜占庭攻击的车辆。同时，在AFL聚合时，重点关注性能较优的车辆，以提升系统的准确性与安全性。本文提出了一种基于DRL的VEC车辆选择方案。该方案综合考虑了车辆移动性、时变信道条件、时变计算资源、不同数据量、车辆传输信道状态以及拜占庭攻击。仿真结果表明，所提方案有效提升了全局模型的安全性与准确性。