Stackelberg Game-Based Hierarchical Incentive Mechanism for Clustered Vehicular Federated Learning

Clustered vehicular federated learning (CVFL) facilitates data sharing and collaborative decision-making among vehicles, thus refining traffic behavior and demonstrating the immense potential for transforming intelligent transportation systems into a reality. However, non-independent and identically distributed data and diverse model requirements among vehicular clients hinder the feasibility of a one-size-fits-all model. Besides, “selfish” vehicular clients may be unwilling to participate in learning tasks because of the huge resource consumption of the training process. To address these challenges, in this paper, we first group local models using the adaptive K-means-based model grouping method and then aggregate the models within each group to generate CVFL models for subsequent multi-model training. Secondly, we propose a dynamic matching-based clustering method based on the local data quality and similarity to achieve efficient vehicular client clustering. Subsequently, a meticulously crafted hierarchical incentive mechanism, grounded in a three-stage Stackelberg game, is introduced to incentivize both cluster heads and members in a layered fashion, with the initiation stemming from the CVFL server. To determine the optimal strategies for the three-stage game, an iterative algorithm is proposed, and near-optimal analytical solutions are obtained with reduced complexity. The simulation results demonstrate that our CVFL system, augmented with the hierarchical incentive mechanism, can effectively motivate multiple clusters to train multiple models in parallel, thus improving overall efficiency.