Asynchronous Federated Deep-Reinforcement-Learning-Based Dependency Task Offloading for UAV-Assisted Vehicular Networks
Si Shen, Guojiang Shen, Zhehao Dai, Kaiyu Zhang, Xiangjie Kong, Jianxin Li
Abstract
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a valuable supplement to conventional MEC, offering unique advantages in temporary or emergency scenarios. However, the integration of UAVs into MEC introduces new challenges. First, the existing UAV-empowered MEC frameworks predominantly have not accounted for dependencies between the computing tasks. Second, the limited coverage area of UAVs and the sparse distribution of end devices result in a scarcity of training samples, hindering the effectiveness of data-driven approaches. To address these challenges, we introduce a novel UAV-assisted task offloading scheme designed to minimize the average task execution delay and energy consumption in vehicular networks. Initially, we craft a dependency-aware UAV-assisted MEC framework, where the task dependency and priority models are meticulously developed to illustrate the associations between the tasks. Subsequently, we devise an asynchronous federated deep reinforcement learning-based task offloading algorithm, incorporating an asynchronous federated optimization mechanism to enhance the data diversity, with a premise of ensuring data privacy. Our method has been rigorously tested using real traffic flow data and a directed acyclic graph task data set. Comparative experiments highlight the superiority of our framework, showcasing a substantial reduction in the average task delay and energy consumption.