An Entropy-Based Privacy-Preserving Federated Deep Reinforcement Learning Framework for Task Offloading in Vehicular Edge Computing Networks

With the rapid evolution of 5G and the ongoing development of 6G technologies, the Internet of Vehicles (IoV) is expected to play a critical role in next-generation intelligent transportation systems. Applications such as autonomous driving, augmented reality, and smart mobility not only require ultra-low latency and high computational efficiency, but also demand enhanced trustworthiness and privacy assurance. To address these demands, Vehicular Edge Computing (VEC) has emerged as a foundational paradigm for 6G-IoT, enabling intelligent services by offloading tasks from vehicles to edge nodes. However, task offloading in IoV-VEC systems still faces critical challenges, including the need for responsible AI decision-making under dynamic network conditions and the protection of sensitive vehicular data. This paper proposes FedVTO, a privacy-preserving federated vehicle task offloading framework that integrates Federated Learning (FL) and Deep Reinforcement Learning (DRL) to optimize task offloading decisions and resource allocation strategies in VEC networks. By incorporating information entropy models and dynamically adjusting weighting parameters using an entropy-based method within a three-tier architecture (vehicles, roadside units, and cloud server), FedVTO minimizes latency, energy consumption, and privacy leakage. Experimental results show that FedVTO significantly improves task offloading efficiency and mitigates privacy risks compared to traditional methods in dynamic VEC environments.