基于联邦元学习的无人机辅助VEC能量延迟权衡计算卸载方法

IF 9.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Chunlin Li;Chaoyue Deng;Yong Zhang;Shaohua Wan
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引用次数: 0

摘要

联邦学习(FL)通过保护隐私,为无人机辅助车辆边缘计算(VEC)的计算卸载提供了一种适用的解决方案。然而,客户的异质性给模型的泛化带来了挑战。因此,我们提出一个联邦元学习(FML)框架来解决无人机辅助VEC的计算卸载问题。本文主要研究由于交通拥塞导致的临时热点区域的计算卸载问题。首先,我们构造了一个具有能量延迟权衡的计算卸载问题,并将其转化为马尔可夫决策过程(MDP)。然后,我们利用FML训练不同车辆的个性化模型,同时增强泛化,提出了一种基于图神经网络的FL概率嵌入Actor-critic RL (GFL-PEARL)算法。我们将上下文建模为有向无环图(DAG),并使用GNN重构PEARL算法的推理网络,以充分提取上下文之间的相关性。在FML训练过程中动态调整任务优先级,提高采样效率。最后,通过仿真和物理实验验证了算法的性能。实验结果表明,与基准算法相比,该算法可将平均成本和任务超时率分别降低31%和56%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Federated Meta-Learning Based Computation Offloading Approach With Energy-Delay Tradeoffs in UAV-Assisted VEC
Federated learning (FL) provides an applicable solution for computation offloading in Unmanned Aerial Vehicle(UAV)-assisted Vehicular Edge Computing (VEC) by preserving privacy. However, the heterogeneity of clients brings challenges to the generalization of models. Therefore, we propose a federated meta-learning (FML) framework to solve computation offloading for UAV-assisted VEC. In this paper, we are concerned with computation offloading of temporary hotspot regions due to traffic congestion. First, we construct a computation offloading problem with energy-delay tradeoffs and convert the problem to a Markov Decision Process (MDP). Then, we use FML to train personalized models for different vehicles while enhancing the generalization, we propose a Graph neural network-based FL Probabilistic Embedding for Actor-critic RL (GFL-PEARL) algorithm. We model the context as a Directed Acyclic Graph (DAG) and use GNN to reconstruct the inference network of the PEARL algorithm to extract the correlation between contexts fully. We dynamically adjust the task priority during the FML training process to improve the sampling efficiency. Finally, we verify the performance of the algorithm through simulation and physical experiments. Experimental results show that our algorithm can reduce average cost and task overtime rate by 31% and 56% respectively compared with the benchmarks.
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
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