Federated deep reinforcement learning for task offloading and resource allocation in mobile edge computing-assisted vehicular networks

【Author】 Zhao, Xu; Wu, Yichuan; Zhao, Tianhao; Wang, Feiyu; Li, Maozhen

【Source】JOURNAL OF NETWORK AND COMPUTER APPLICATIONS

【影响因子】7.574

【Abstract】Mobile edge computing (MEC) enables computation intensive applications in the Internet of Vehicles (IoV) to no longer be limited by device resources. However, the lack of an effective task scheduling strategy will seriously affect users' quality of experience (QoE). In this paper, a task type-based task offloading and resource allocation strategy is proposed to reduce delay and energy consumption during task execution. First, we establish communication, computing, and system cost models based on task offloading schemes, and model the joint optimization problem of task offloading and resource allocation as a Markov decision process. The utility function is obtained based on the task completion rate and the system cost. Second, an algorithm framework based on multi-agent deep deterministic policy gradient (MADDPG) is designed to solve the difficulty that traditional single-agent reinforcement learning algorithms are difficult to converge in a dynamic environment. In distributed scenarios, the proposed framework can also reduce system costs while handling more tasks. Finally, federated learning is introduced in the training process to reduce the impact of non-IID data while protecting privacy. Simulation results show that the proposed algorithm can effectively improve system processing efficiency and reduce device energy consumption compared to the popular reinforcement learning algorithms.

【Keywords】Internet of vehicles; Mobile edge computing; Deep reinforcement learning; Federated learning; Task offloading; Resource allocation

【发表时间】2024 SEP

【收录时间】2024-08-10

【文献类型】实验仿真

【主题类别】

区块链应用-实体经济-物联网