Federated learning merges with v2x privacy protection

Abstract

Vehicle-to-Everything (V2X) communication systems are poised to revolutionize road safety, traffic efficiency, and driver experience through real-time data exchange among vehicles and infrastructure. However, ensuring the privacy and security of sensitive vehicular data remains a critical challenge. Federated Learning (FL) emerges as a promising paradigm to address these concerns by enabling collaborative model training without centralized data aggregation. This paper explores the integration of FL techniques into V2X environments, where vehicles act as decentralized participants in model training processes. We discuss the unique challenges and opportunities presented by V2X scenarios, such as intermittent connectivity and diverse data distributions across vehicles. Through a detailed review of existing FL methodologies and their adaptation to V2X settings, this paper proposes practical solutions for privacy-preserving model aggregation and efficient learning across dynamic vehicular networks. Case studies and simulation results illustrate the feasibility and benefits of FL in enhancing V2X applications while safeguarding user privacy. This study contributes to the growing field of secure and collaborative machine learning in connected vehicle environments, paving the way for safer and smarter transportation systems.