Advancing Federated Learning in IoV: GNN-Based Trajectory Prediction and Privacy Protection

With the rapid development of Vehicle-Road-Network integration, trajectory prediction has played a critical role in traffic efficiency enhancement and driving safety. Feasible trajectory prediction can be achieved by deep learning and big data methods. However, distributed data in different vehicles leads to Isolated Data Island issue, which becomes a challenge for effective model updating and safe data sharing. In this paper, we propose a novel algorithm for Internet of Vehicle (IoV) trajectory prediction and privacy protection under the secure federated learning (FL) scheme. It utilizes graph neural network (GNN) to effectively represent vehicle nodes and their feature information, which satisfies communication efficiency and privacy protection simultaneously with FL, named FedGNN. First, update vehicle feature as embedding with GNN local model in Client-Side for trajectory prediction. Then, FL Server-Side introduces a secure aggregation approach to generate global model to protect vehicle-to-vehicle (V2V) communication privacy. Simulation on NGSIM and highD datasets and comparison with baseline algorithms verify that FedGNN has advantages in both trajectory prediction performance and timeliness.