An Authentication Protocol for Federated Learning With Blockchain in Consumer Electronic Assisted Autonomous Driving Environments

Abstract

Consumer electronics play a crucial role in the automotive sector by providing the essential hardware infrastructure necessary for developing autonomous vehicles. These vehicles significantly enhance driving comfort and accessibility, particularly benefiting specific populations such as the elderly and individuals with disabilities. Researchers utilize federated learning, employing homomorphic encryption and differential privacy techniques; however, these methods can impede efficiency. Traditional centralized federated learning presents a risk of single points of failure. To address this issue, we propose an authentication protocol for federated learning, integrated with blockchain technology, within consumer electronics-supported autonomous driving environments. This protocol enhances vehicle authentication efficiency while prioritizing data security and user privacy. Formal analysis verifies the integrity of the protocol. Experimental results demonstrate that the communication overhead of our protocol is reduced by 40.3% compared to existing similar authentication protocols.