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.

翻译：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，支持时延敏感型车载应用。然而，实际车载工作负载通常被建模为具有复杂依赖关系的异质有向无环图（DAG）任务，使得联合卸载与资源分配极具挑战性。此外，分布式多接入边缘计算（MEC）部署在协作训练基于学习的策略时引发了隐私问题。本文提出一种基于GAT-Seq2Seq建模的联邦元深度强化学习框架（FedMAGS），用于VEC系统中的异质任务卸载。该方法利用图注意力网络捕获DAG依赖关系，通过基于Seq2Seq的策略生成结构化的卸载决策，并采用联邦元学习实现在分布式MEC服务器间的快速自适应，且无需共享原始数据。大量仿真结果表明，与最先进的基线方法相比，FedMAGS具有更快的收敛速度、更低的执行延迟和更好的可扩展性。此外，联邦设计在降低通信开销的同时保护了数据隐私，使该框架非常适合动态且大规模的车载边缘计算环境。