Asynchronous Federated Deep Reinforcement Learning-Based Dependency Task Offloading for UAV-Assisted Vehicular Networks

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. Firstly, existing UAV-empowered MEC frameworks predominantly have not accounted for dependencies between computing tasks. Secondly, 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 task dependency and priority models are meticulously developed to illustrate the associations between tasks. Subsequently, we devise an asynchronous federated deep reinforcement learning-based task offloading algorithm, incorporating an asynchronous federated optimization mechanism to enhance 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 dataset. Comparative experiments highlight the superiority of our framework, showcasing a substantial reduction in the average task delay and energy consumption.