FL-MADDPG-IoV: federated multi-agent deep reinforcement learning for task offloading in 6G IoV using multi-tier UAV-enabled edge computing
摘要
Efficient and privacy-aware task offloading remains a critical challenge for 6 G-enabled Internet of Vehicles (IoV) due to highly dynamic vehicular mobility, heterogeneous edge resources, and stringent latency and energy constraints. To address these challenges, an intelligent task offloading framework based on federated learning-assisted multi-agent deep deterministic policy gradient Internet of Vehicles (FL-MADDPG-IoV) is developed for a multi-tier edge computing architecture comprising roadside units, unmanned aerial vehicle-based edge servers, and macro base stations. The framework integrates federated reinforcement learning with hierarchical multi-agent coordination, enabling distributed edge agents to collaboratively optimize offloading policies while preserving data privacy by eliminating the exchange of raw data. The learning process is evaluated on a large-scale dataset comprising 184,482 samples with six statistical features, demonstrating adaptability across varying vehicular densities and network conditions. Simulation results indicate substantial performance improvements over existing deep reinforcement learning and federated learning approaches. Specifically, task latency is reduced by