Federated Multi-Agent DRL for Task Offloading in Vehicular Edge Computing

With the expansion of vehicle-to-everything (V2X) networks and the rising demand for intelligent services, vehicle edge computing encounters heightened requirements for more efficient task offloading. This study proposes a task offloading technique that utilizes federated collaboration and multi-agent deep reinforcement learning to reduce system latency and energy consumption. The task offloading issue is formulated as a Markov decision process (MDP), and a framework utilizing the Multi-Agent Dueling Double Deep Q-Network (MAD3QN) is developed to facilitate agents in making optimal offloading decisions inside intricate environments. Secondly, Federated Learning (FL) is implemented during the training phase, leveraging local training outcomes from many vehicles to enhance the global model, thus augmenting the learning efficiency of the agents. Experimental results indicate that, compared to conventional baseline algorithms, the proposed method decreases latency and energy consumption by at least 10% and 9%, respectively, while enhancing the average reward by at least 21%.