Federated Reinforcement Learning-Empowered Task Offloading for Large Models in Vehicular Edge Computing
Huaming Wu, Anqi Gu, Yonghui Liang
Abstract
Vehicular Edge Computing (VEC) has garnered substantial attention owing to its capacity to provide ample computational resources for computation-intensive tasks. However, how to flexibly allocate computing tasks within vehicles and efficiently manage the resources consumed by tasks has emerged as a challenge. To tackle this issue, this research advances the proposition of employing an auxiliary vehicle (AV) for task offloading and introduces a novel Auxiliary Vehicle Algorithm (AVA). AVA integrates both federated learning and multi-agent reinforcement learning to fully utilize computing resources in the vehicular environment, and simultaneously achieves task delay reduction, energy consumption minimization, and task completion rate augmentation. Moreover, we establish a federated learning framework to judiciously determine the proportion of resource allocation of AV through the implementation of inventive mechanisms. Experiment results validate that our approach not only leads to the improvement of key system performance indicators, but also ensures the comprehensive exploitation of the computing resources of mobile vehicles.