Graph-Based Federated Multiagent DRL for Semantic and Intent-Aware V2X Communication

The coexistence of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication in 6G-enabled vehicular networks introduces complex challenges in spectrum sharing, semantic prioritization, and real-time coordination. To address these issues, we propose G-FEDMAP, a graph-based federated multi-agent deep reinforcement learning framework that supports semantic- and intent-aware resource allocation in distributed vehicle-to-everything (V2X) environments. G-FEDMAP integrates GraphSAGE-based spatiotemporal embeddings, shared actor–centralized critic multi-agent proximal policy optimization (MAPPO) training under a centralized training and decentralized execution (CTDE) paradigm, and event-adaptive reward shaping guided by user intent profiles. To preserve data locality and promote scalable collaboration, federated policy coordination is introduced across geographically partitioned vehicular domains. The system dynamically rebalances semantic priorities using intent reprioritization weights, enabling responsiveness to mission-critical context. We evaluate G-FEDMAP in a federated urban vehicular network comprising multiple regions with high agent density, dynamic event intents, and shared spectrum constraints. The proposed framework is tested under diverse communication and traffic conditions, and compared against MAPPO, graph neural network-MAPPO (GNN-MAPPO), and federated PPO variants. G-FEDMAP demonstrates improved V2V delivery success, higher semantic retention, greater intent satisfaction, and better coexistence of V2V and V2I links through graph-based scheduling and federated policy adaptation. These results position G-FEDMAP as a reliable and trustworthy AI-driven solution for future 6G-internet of things (6G-IoT) vehicular networks.