Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous
  Federated Learning in Vehicular Edge Computing
Cui Zhang Xiao Xu Qiong Wu Pingyi Fan Qiang Fan Huiling Zhu Jiangzhou Wang
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.