  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.
