针对车载网络中车辆移动导致训练失败的问题，提出自适应选择策略提升联邦学习效率。
Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Network
- 根据车辆实时参与成功率动态选择参与联邦学习的车辆。
- 相比基线方法，训练收敛速度提升约28%且损失更低。
- 适合车联网场景下的移动设备联邦学习研究者参考。
本文研究了车载网络中联邦学习的车辆选择问题。提出一种面向移动性的车载联邦学习（MAVFL）方案，车辆在行驶过程中完成联邦学习任务，但部分车辆可能驶离路段导致训练失败。该方案利用实时成功参与率进行车辆选择，并进行收敛性分析，揭示了车辆移动对训练损失的影响。进一步提出基于多臂老虎机的车辆选择算法，以最小化同时考虑训练损失与延迟的效用函数。仿真结果表明，相比基线方法，所提算法可实现约28%更快的收敛速度，且训练性能更优。
原文摘要 · Abstract (English)
In this paper, we study a vehicle selection problem for federated learning (FL) over vehicular networks. Specifically, we design a mobility-aware vehicular federated learning (MAVFL) scheme in which vehicles drive through a road segment to perform FL. Some vehicles may drive out of the segment which leads to unsuccessful training. In the proposed scheme, the real-time successful training participation ratio is utilized to implement vehicle selection. We conduct the convergence analysis to indicate the influence of vehicle mobility on training loss. Furthermore, we propose a multi-armed bandit-based vehicle selection algorithm to minimize the utility function considering training loss and delay. The simulation results show that compared with baselines, the proposed algorithm can achieve better training performance with approximately 28\% faster convergence.
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