Federated Learning with Differential Privacy for Resilient Vehicular Cyber Physical Systems

Abstract

Vehicular cyber physical systems (VCPS) will play a vital role in the quest to develop intelligent transportation systems (ITS) and smart cities around the world. Consequently, researchers in academia, industry and government continue to leverage on emerging technologies like software defined networking (SDN), blockchain, cloud computing and machine learning (ML) to improve the overall efficiency of these intelligent systems. Recently, mobile edge computing (MEC) has been used to enhance content caching and efficient resource allocation and therefore advance the development of data-intensive and delay-constrained applications that improve the driving experience in VCPS. Security and privacy concerns that endanger the safety of lives and infrastructure necessitate the need to use federated learning (FL), a distributed ML algorithm that employs learning at the edge to ensure that data remains at the different vehicles and thus enhance greater efficiency. In this paper therefore, we propose the use of FL, together with differential privacy to improve the resiliency of VCPS to adversarial attacks in connected vehicles.