Decentralized Identity Management and Privacy-Enhanced Federated Learning for Automotive Systems: A Novel Framework

Federated Learning (FL) has revolutionized collaborative machine learning by decentralizing data processing, enhancing the efficiency of traditional Machine Learning (ML) approaches, and mitigating privacy concerns associated with data exchange. Despite these advantages, security challenges persist, particularly in securely transmitting model updates within vehicular networks and authenticating nodes participating in the protocol. This paper presents an innovative framework that addresses authentication and mobility challenges in automotive systems through the integration of Decentralized Identity Management (IdM) and FL. Highlighting the need for robust authentication in automotive systems, the research concurrently explores avenues to optimize FL performance within this specific context. Through the incorporation of a decentralized authentication mechanism and the establishment of synchronization means, our proposed framework ensures security and synchronization in the transmission of model weights. This comprehensive solution paves the way for notable advancements in collaborative ML in highly dense and distributed contexts, such as the vehicular networks.