GNN-Based Clustered Federated Learning for Hierarchical Vehicular Networks

Using Clustered Vehicular Federated Learning (CVFL) in vehicular networks can enhance model intelligence through distributed collaboration while preserving data privacy. CVFL groups clients with similar data distributions to reduce the impact of non-independent and identically distributed (non-iid) data on federated learning efficiency, thereby supporting realtime and dynamic traffic decision-making. However, in practical applications, the high mobility of vehicles and the intermittent connectivity of communication links make it challenging to flexibly determine the number of clusters and the vehicles within each cluster. To address these issues, we propose a Graph Neural Network(GNN)-based clustering scheme called GNN-CVFL. Our proposed scheme includes two modules: clustering and resource allocation. In the clustering module, we treat the clustering problem of vehicle clients as a node classification problem in GNN, using a GNN-based method to cluster clients accurately based on their data distribution. The proposed method can automatically determine the number of clusters and the vehicles within each cluster in real-time. Moreover, based on the clustering results, We use the Lagrangian relaxation method to dynamically allocate available bandwidth resources and CPU frequencies to minimize system latency, ensuring efficient and flexible service for all vehicles. Numerical results demonstrate the feasibility and efficiency of our proposed scheme.