Federated MADDPG-Based Collaborative Scheduling Strategy in Vehicular Edge Computing

In Vehicular Edge Computing (VEC), vehicles offload computational tasks to Roadside Units (RSUs) equipped with edge servers to achieve efficient processing. Considering that vehicles switch connections between RSUs during high-speed movement, obtaining the state information of other RSUs is crucial for achieving global collaborative decision-making. However, frequent sharing of RSUs’ state data during the training of scheduling models may result in privacy leakage risks. To address this issue, we federally train a joint scheduling model for task offloading and resource allocation without the need for state sharing among RSUs. We prove that the proposed task offloading problem influenced by resource allocation is a strict multi-node non-cooperative potential game problem, and use the potential function as the reward function for Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Finally, we propose the Fed-MADDPG algorithm to find the equilibrium point of task offloading and apply the gradient descent method and the Lagrange multiplier method to maximize the average task completion rate among RSUs under constraints, ensuring the framework has optimal computational and transmission performance. We conduct simulation experiments using real-world datasets, and the results show that this method has superior performance compared to previous approaches.