Distributed Decision-making System for Intelligent Connected Vehicles Based on Federated Learning
Current distributed decision-making for intelligent connected vehicles is constrained by model heterogeneity, high communication overhead, and data privacy concerns, which impede efficient multi-vehicle collaboration in wireless vehicular communication environments. As reliable information exchange and low-latency signal transmission become increasingly important in intelligent transportation systems and electromagnetic communication networks, this paper proposes a distributed decision-making framework integrating Federated Distillation (FD) and Graph Attention Networks (GAT). A local GAT dynamically constructs vehicle adjacency graphs for neighboring perception, while FD replaces conventional parameter sharing with soft-label exchange, significantly reducing communication burden, enabling knowledge transfer across heterogeneous devices, and alleviating computational bottlenecks on resource-limited nodes. Evaluated in the most challenging scenario characterized by maximum vehicle density, dynamic communication delays, packet loss, severe data Non-IIDness, and complex multi-intersection interactions, the proposed method demonstrates superior collaborative performance. The average decision latency is maintained at 91 ms, which is 43 ms lower than that of the traditional FedAvg approach, while the number of uncoordinated behavior triggers is reduced by 50% to only three. These results verify that the proposed framework achieves efficient and privacy-preserving collaborative decision-making while providing a scalable solution for distributed intelligence in wireless vehicular networks and communication-intensive electromagnetic environments.