Federated-Learning-Empowered Collaborative Data Sharing for Vehicular Edge Networks
Xiuhua Li, Luxi Cheng, Chuan Sun, Kwok‐Yan Lam, Xiaofei Wang, Feng Li
Abstract
The Internet of Vehicles connects all vehicles and shares dynamic vehicular data via wireless communications to effectively control vehicles and improve traffic efficiency. However, due to vehicular movement, vehicular data sharing based on conventional cloud computing can hardly realize real-time and dynamic updates. To address these challenges, artificial intelligence (AI)-empowered mobile/multi-access edge computing (MEC) has been regarded as a promising technique for intelligently supporting various vehicular services and applications in proximity of vehicles. In this article, we investigate the issue of collaborative data sharing in vehicular edge networks (VENs) with the deployment of AI-empowered MEC servers. Furthermore, we present a specific mode for collaborative data sharing. Then we propose a novel collaborative data sharing scheme with deep Q-network and federated learning to ensure efficient and secure data sharing in the VEN. Evaluation results demonstrate the effectiveness of the proposed scheme on reducing latency of vehicular data sharing. Finally, we discuss several open issues and future challenges of the AI-empowered VEN.