  Leveraging the computing and sensing capabilities of vehicles, vehicular
federated learning (VFL) has been applied to edge training for connected
vehicles. The dynamic and interconnected nature of vehicular networks presents
unique opportunities to harness direct vehicle-to-vehicle (V2V) communications,
enhancing VFL training efficiency. In this paper, we formulate a stochastic
optimization problem to optimize the VFL training performance, considering the
energy constraints and mobility of vehicles, and propose a V2V-enhanced dynamic
scheduling (VEDS) algorithm to solve it. The model aggregation requirements of
VFL and the limited transmission time due to mobility result in a stepwise
objective function, which presents challenges in solving the problem. We thus
propose a derivative-based drift-plus-penalty method to convert the long-term
stochastic optimization problem to an online mixed integer nonlinear
programming (MINLP) problem, and provide a theoretical analysis to bound the
performance gap between the online solution and the offline optimal solution.
Further analysis of the scheduling priority reduces the original problem into a
set of convex optimization problems, which are efficiently solved using the
interior-point method. Experimental results demonstrate that compared with the
state-of-the-art benchmarks, the proposed algorithm enhances the image
classification accuracy on the CIFAR-10 dataset by 3.18% and reduces the
average displacement errors on the Argoverse trajectory prediction dataset by
10.21%.
