Federated Learning Assisted Intelligent IoV Mobile Edge Computing
Haoyu Quan, Qingmiao Zhang, Junhui Zhao
Abstract
As a crucial solution to the insufficient computing resources of device in Internet of Vehicles (IoVs) systems, mobile edge computing (MEC) has received widespread attention, especially for tackling delay-sensitive tasks in IoVs. This paper focuses on a multi-roadside units (RSUs) multi-vehicle IoV MEC system with different task delay thresholds. To enhance the system performance in terms of task completion rate, service delay, and energy consumption, a hybrid multi-agent deep reinforcement learning algorithm (HMADRL) based adaptive joint optimization scheme was proposed for computation offloading and resource allocation strategies. Further, a centralized computation offloading and distributed resource allocation framework is designed to reduce communication overhead between multiple agents, and federated learning (FL) technology is used to protect user privacy and accelerate training. The numerical results validate that our scheme improves the performance of IoV MEC system significantly while satisfying system resource and task delay constraints.