With the rapid development of intelligent vehicles and Intelligent Transport Systems (ITS), the sensors such as cameras and LiDAR installed on intelligent vehicles provides higher capacity of executing computation-intensive and delay-sensitive tasks, thereby raising deployment costs. To address this issue, Vehicular Edge Computing (VEC) has been proposed to process data through Road Side Units (RSUs) to support real-time applications. This paper focuses on the Age of Information (AoI) as a key metric for data freshness and explores task offloading issues for vehicles under RSU communication resource constraints. We adopt a Multi-agent Deep Reinforcement Learning (MADRL) approach, allowing vehicles to autonomously make optimal data offloading decisions. However, MADRL poses risks of vehicle information leakage during communication learning and centralized training. To mitigate this, we employ a Federated Learning (FL) framework that shares model parameters instead of raw data to protect the privacy of vehicle users. Building on this, we propose an innovative distributed federated learning framework combining Graph Neural Networks (GNN), named Federated Graph Neural Network Multi-Agent Reinforcement Learning (FGNN-MADRL), to optimize AoI across the system. For the first time, road scenarios are constructed as graph data structures, and a GNN-based federated learning framework is proposed, effectively combining distributed and centralized federated aggregation. Furthermore, we propose a new MADRL algorithm that simplifies decision making and enhances offloading efficiency, further reducing the decision complexity. Simulation results demonstrate the superiority of our proposed approach to other methods through simulations.

翻译：随着智能车辆与智能交通系统（ITS）的快速发展，智能车辆搭载的摄像头、激光雷达等传感器为执行计算密集型和时延敏感型任务提供了更强的能力，但也带来了更高的部署成本。为解决这一问题，车载边缘计算（VEC）被提出，通过路侧单元（RSU）处理数据以支持实时应用。本文以信息年龄（AoI）作为数据新鲜度的关键度量指标，探讨在RSU通信资源约束下车辆的任务卸载问题。我们采用多智能体深度强化学习（MADRL）方法，使车辆能够自主做出最优的数据卸载决策。然而，MADRL在通信学习和集中式训练过程中存在车辆信息泄露的风险。为缓解此问题，我们采用联邦学习（FL）框架，通过共享模型参数而非原始数据来保护车辆用户的隐私。在此基础上，我们提出一种创新的分布式联邦学习框架，结合图神经网络（GNN），命名为联邦图神经网络多智能体强化学习（FGNN-MADRL），以优化整个系统的AoI。本研究首次将道路场景构建为图数据结构，并提出一种基于GNN的联邦学习框架，有效结合了分布式与集中式的联邦聚合。此外，我们提出一种新的MADRL算法，该算法简化了决策过程并提升了卸载效率，进一步降低了决策复杂度。仿真结果表明，我们提出的方法相较于其他方法具有优越性。