Graph-Based Federated Multiagent DRL for Semantic and Intent-Aware V2X Communication
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".