论文标题
资源约束的车辆边缘联合学习与高移动连接的车辆
Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles
论文作者
论文摘要
本文提出了一个车辆边缘联合学习（VEFL）解决方案，其中边缘服务器利用高度移动的连接车辆（CVS'）在船中央处理单元（CPU）和本地数据集来培训全球模型。收敛分析表明，VEFL训练损失取决于在间歇性车辆到基础结构（V2I）无线链接上CVS训练有素的模型的成功接收。在整个设备参与案例（FDPC）中，由于CVS的数据集尺寸和SOJOURN周期，Edge Server在完整的设备参与案例（FDPC）中，Edge Server汇总了客户端模型参数，同时选择部分设备参与案例（PDPC）中的CVS子集。然后，我们设计了关节VEFL和无线电访问技术（RAT）参数在延迟，能源和成本限制下优化问题，以最大程度地提高成功接受本地训练的模型的可能性。考虑到优化问题是NP-HARD，我们将其分解为VEFL参数优化的子问题，考虑到估计的最差案例周期，延迟和能量费用以及在线大鼠参数优化子问题。最后，进行了广泛的模拟，以在现实的显微镜迁移率模型下使用实用的5G新无线电（5G-NR）大鼠验证拟议溶液的有效性。
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.
