Selective Federated Learning for On-Road Services in Internet-of-Vehicles

Abstract

The Internet-of-Vehicles (IoV) can make driving safer and bring more services to smart vehicle (SV) users. Specif-ically, with IoV, the road service provider (RSP) can collaborate with SVs to provide high-accurate on-road information-based services by implementing federated learning (FL). Nonetheless, SVs' activities are very diverse in IoV networks, e.g., some SVs move frequently while other SVs are occasionally disconnected from the network. Consequently, obtaining information from all SVs for the learning process is costly and impractical. Furthermore, the quality-of-information (QoI) obtained by SVs also dramatically varies. That makes the learning process from all SVs simultaneously even worse when some SVs have low QoI. In this paper, we propose a novel selective FL approach for an IoV network to address these issues. Particularly, we first develop an SV selection method to determine a set of active SVs based on their location significance. In this case, we adopt a K-means algorithm to classify significant and insignificant areas where the SVs are located according to the areas' average annual daily flow of vehicles. From the set of SVs in the significant areas, we select the best SVs for the FL execution based on the SVs' QoI at each learning round. Through simulation results using a real-world on-road dataset, we observe that our proposed approach can converge to the FL results even with only 10% of active SVs in the network. Moreover, our results reveal that the RSP can optimize on-road services with faster convergence up to 63% compared with other baseline FL methods.