Privacy-Preserving Task Offloading in Vehicular Edge Computing Using Federated Multi-Agent Reinforcement Learning

Vehicular edge computing (VEC) systems face critical challenges in balancing computational efficiency, task delay, and data privacy. This paper presents a Federated MultiAgent Deep Reinforcement Learning (FMADRL) framework to achieve privacy-preserving task offloading in dynamic vehicular networks. The proposed method leverages federated learning to collaboratively train task offloading policies across vehicles, Mobile Edge Computing (MEC) servers, and the cloud, ensuring that sensitive data remains localized while enabling global optimization. A novel reward function is designed to balance task completion, delay, energy consumption, and privacy constraints, while a federated actor-critic model ensures robust decision-making under dynamic network conditions. Simulation results demonstrate that the FMADRL framework significantly reduces average task delay and energy consumption by 30% and 25%, respectively, compared to traditional methods, while maintaining data privacy. These findings underscore the potential of FMADRL to enhance the scalability, efficiency, and security of VEC systems in intelligent transportation networks.