  Federated Learning (FL) can protect the privacy of the vehicles in vehicle
edge computing (VEC) to a certain extent through sharing the gradients of
vehicles' local models instead of local data. The gradients of vehicles' local
models are usually large for the vehicular artificial intelligence (AI)
applications, thus transmitting such large gradients would cause large
per-round latency. Gradient quantization has been proposed as one effective
approach to reduce the per-round latency in FL enabled VEC through compressing
gradients and reducing the number of bits, i.e., the quantization level, to
transmit gradients. The selection of quantization level and thresholds
determines the quantization error, which further affects the model accuracy and
training time. To do so, the total training time and quantization error (QE)
become two key metrics for the FL enabled VEC. It is critical to jointly
optimize the total training time and QE for the FL enabled VEC. However, the
time-varying channel condition causes more challenges to solve this problem. In
this paper, we propose a distributed deep reinforcement learning (DRL)-based
quantization level allocation scheme to optimize the long-term reward in terms
of the total training time and QE. Extensive simulations identify the optimal
weighted factors between the total training time and QE, and demonstrate the
feasibility and effectiveness of the proposed scheme.
