用联邦元学习解决车载边缘计算中异构任务的隐私保护调度问题
Heterogeneous Tasks Offloading in Vehicular Edge Computing: A Federated Meta Deep Reinforcement Learning Approach
- 基于图注意力网络与序列生成模型，建模任务依赖关系并生成调度决策
- 相比现有方法，收敛更快、延迟更低，支持大规模动态环境
- 联邦学习保障数据隐私，适合分布式车载边缘系统部署
车载边缘计算（VEC）通过将计算密集型任务卸载至邻近边缘服务器，支持低延迟车联网应用。然而，真实车载工作负载通常表现为具有复杂依赖结构的异构有向无环图（DAG）任务，使得联合卸载与资源分配极具挑战。此外，分布式多接入边缘计算（MEC）部署带来隐私风险，尤其在协同训练基于学习的策略时。本文提出一种融合图注意力网络与序列到序列建模的联邦元深度强化学习框架（FedMAGS），用于异构任务卸载。该方法利用图注意力网络捕捉任务间依赖，采用基于Seq2Seq的策略生成结构化卸载决策，并通过联邦元学习实现跨分布式MEC节点的快速适应，无需共享原始数据。大量仿真表明，相比先进基线方法，FedMAGS具备更快收敛速度、更低执行延迟和更优可扩展性。同时，其联邦设计有效保护数据隐私并降低通信开销，适用于动态且大规模的车载边缘环境。
原文摘要 · Abstract (English)
Vehicular edge computing (VEC) enables latency-sensitive vehicular applications by offloading computation-intensive tasks to nearby edge servers. However, real-world vehicular workloads are typically modeled as heterogeneous directed acyclic graph (DAG) tasks with complex dependency structures, making joint offloading and resource allocation highly challenging. Moreover, distributed MEC deployment raises privacy concerns when collaboratively training learning-based policies. In this paper, we propose a Federated Meta Deep Reinforcement Learning framework with GAT-Seq2Seq modeling (FedMAGS) for heterogeneous task offloading in VEC systems. The proposed approach leverages Graph Attention Networks to capture DAG dependencies, a Seq2Seq-based policy to generate structured offloading decisions, and federated meta-learning to enable fast adaptation across distributed MEC servers without sharing raw data. Extensive simulations demonstrate that FedMAGS achieves faster convergence, lower execution delay, and better scalability compared with state-of-the-art baselines. In addition, the federated design preserves data privacy while reducing communication overhead, making the framework well suited for dynamic and large-scale VEC environments.
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