Online Auction for Federal Learning Client Selection in IoV

The integration of vehicular networks with Machine Learning (ML) is driving the advancement and intelligence of future vehicular systems. As a core technology in the IoVs, Vehicular Edge Computing (VEC) leverages the computational and communication resources of both vehicles and edge servers, enabling model training closer to the data source. Federal Learning (FL) has shown great promise in training large-scale ML models without exposing raw data. However, many vehicles are reluctant to participate in FL training due to high resource demands and the inherent mobility challenges of vehicular networks. To address this issue, this paper proposes an FL auction framework that incentivizes vehicle participation by maximizing the utility of the FL platform. Specifically, our approach factors in the basic utility, average reward and tolerance delay of dynamic vehicles to determine their bidding intent. Additionally, an online auction-based client selection algorithm is proposed that ensures individual rationality for vehicles, coupled with a reward function based on model accuracy to further encourage participation in FL training. Simulation results demonstrate the effectiveness of the proposed algorithm, showing a 38.9% improvement in platform utility and a 31.3% reduction in average payments compared to the Online Auction (OA) algorithm.