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%.

翻译：利用车辆的计算与感知能力，车载联邦学习（VFL）已被应用于联网车辆的边缘训练。车辆网络的动态性与互联性为利用直接的车对车（V2V）通信提供了独特机遇，从而提升VFL训练效率。本文构建了一个随机优化问题以优化VFL训练性能，同时考虑车辆的能量约束与移动性，并提出一种V2V增强的动态调度（VEDS）算法予以求解。VFL的模型聚合需求与移动性导致的有限传输时间共同构成了阶梯状目标函数，这给问题求解带来了挑战。为此，我们提出一种基于导数的漂移加惩罚方法，将长期随机优化问题转化为在线混合整数非线性规划（MINLP）问题，并通过理论分析界定了在线解与离线最优解之间的性能差距。进一步对调度优先级进行分析，将原问题简化为一系列凸优化问题，并采用内点法高效求解。实验结果表明，相较于现有先进基准方法，所提算法在CIFAR-10数据集上的图像分类精度提升了3.18%，在Argoverse轨迹预测数据集上的平均位移误差降低了10.21%。