New AI Framework Cuts Data Traffic in Connected Cars by Half

Vehicular Edge Computing (VEC) is a key enabler of real-time intelligence in next-generation transportation systems. However, conventional Federated Learning (FL) in VEC typically depends on static edge-server aggregation, resulting in high communication overhead, increased latency, and poor responsiveness under dynamic mobility. To overcome these challenges, we propose Proximity-Aware Federated Learning (PA-FL), a decentralized framework that integrates vehicle-to-vehicle (V2V) collaboration and edge-assisted synchronization to enhance learning efficiency, scalability, and robustness. PA-FL introduces three core innovations: (i) Collaborative Local Aggregation, where vehicles perform proximity-based model fusion before forwarding updates to the edge, reducing uplink traffic and accelerating convergence; (ii) Adaptive Neighbor Selection, which dynamically filters peers based on spatiotemporal proximity and link stability to ensure context-relevant learning; and (iii) Context-Aware Synchronization, which adjusts aggregation frequency based on vehicular density and mobility to improve energy efficiency and learning consistency. Extensive experiments demonstrate that PA-FL achieves an average accuracy of 87.08% ± 0.49, surpassing state-of-the-art FL baselines by over 13% in accuracy and 11% in F1 score. It reduces task failure rates across all proximity ranges and lowers per-round energy consumption to 0.038 J, achieving a 6× improvement in communication efficiency. Delay per communication round is also reduced to 0.85 seconds, supporting real-time responsiveness. These results validate PA-FL as a resilient and scalable framework for symbiotic FL where vehicles collaboratively learn from local context while contributing to global intelligence in AI-integrated, 6G-enabled vehicular edge environments.