Privacy-Preserving Reinforcement Learning Framework for V2x Resource Management

Abstract

One of the pillars of the intelligent transportation systems can be defined as vehicle-to-Everything (V2X) communication, since this enables a real-time communication of information among vehicles, roadside units (RSUs), and the infrastructure. However, the efficient and secure resource distribution of the V2X network is not a simple task, which is conditioned by the highly dynamic nature of the automotive environment, spectrum deficiency, and further privacy issues. Traditional centralized approaches to education, though effective in terms of performance, cannot be applied to large-scale V2X applications in terms of overhead of communication and single point of weakness and the threat of data leakage. The article proposes a safe federated reinforcement learning (SecAgg-FRL) framework of privacy-guaranteed and scalable allocation of resources in V2X systems. Within the provided system, each V2X node becomes informed of a local reinforcement learning agent depending on the traffic, mobility, and channel state of the particular node. Raw data are not exchanged but instead, only masked model updates are sent to a federated server where secure aggregation is enforced to get the global model without revealing any single contribution. The framework can collectively maximize usage of spectrum, latency and interference reduction, and it can withstand inference and model inversion attacks. The simulation findings suggest that the designed SecAgg-FRL framework can achieve spectrum utilization that is close to that of centralized RL, with low latency, and scales well as the number of clients increases, as well as provides reasonably high level of privacy protection with little overhead. These results illustrate why the SecAgg-FRL is a potentially useful solution to resource management in next generation V2X networks in real-time, securely and scalably.

Topics & Concepts