Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Network
This addresses efficient federated learning for vehicular networks, but it is incremental as it builds on existing FL and bandit methods.
The paper tackles the problem of vehicle selection for federated learning in vehicular networks, where mobility causes training failures, and proposes a multi-armed bandit-based algorithm that achieves approximately 28% faster convergence compared to baselines.
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.