This paper proposes a vehicular edge federated learning (VEFL) solution, where an edge server leverages highly mobile connected vehicles' (CVs') onboard central processing units (CPUs) and local datasets to train a global model. Convergence analysis reveals that the VEFL training loss depends on the successful receptions of the CVs' trained models over the intermittent vehicle-to-infrastructure (V2I) wireless links. Owing to high mobility, in the full device participation case (FDPC), the edge server aggregates client model parameters based on a weighted combination according to the CVs' dataset sizes and sojourn periods, while it selects a subset of CVs in the partial device participation case (PDPC). We then devise joint VEFL and radio access technology (RAT) parameters optimization problems under delay, energy and cost constraints to maximize the probability of successful reception of the locally trained models. Considering that the optimization problem is NP-hard, we decompose it into a VEFL parameter optimization sub-problem, given the estimated worst-case sojourn period, delay and energy expense, and an online RAT parameter optimization sub-problem. Finally, extensive simulations are conducted to validate the effectiveness of the proposed solutions with a practical 5G new radio (5G-NR) RAT under a realistic microscopic mobility model.
翻译:本论文提出了车载边缘联邦学习(VEFL)解决方案,其中边缘服务器利用高度移动的连接车辆(CVs)的中央处理单元(CPUs)和本地数据集来训练全局模型。收敛分析表明,VEFL训练丢失取决于CVs经过间歇性的车-基础设施(V2I)无线连接成功接收训练模型。由于高度移动,在全设备参与案例(FDPC)中,边缘服务器根据CVs的数据集大小和逗留时间进行加权组合,聚合客户端模型参数,而在部分设备参与案例(PDPC)中选择CVs的子集。然后,考虑到优化问题是NP困难的,我们将其分解为VEFL参数优化子问题和在线RAT参数优化子问题来解决延迟,能耗和成本约束下的联合优化问题,从而最大化成功接收本地训练模型的概率。最后,使用实际的5G新无线电(5G-NR)RAT和实际微观移动模型进行广泛的模拟,验证了所提出解决方案的有效性。