Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction With Blockchain

Abstract

Federated learning (FL), when integrated with blockchain, facilitates secure data sharing in autonomous driving applications. As vehicle-generated data becomes more granular and complex, the absence of data quality audits raises concerns about multiparty mistrust in trajectory prediction tasks. However, most of the existing research on trajectory prediction focuses on how to improve the model to enhance the prediction accuracy, and lacks the consideration of the privacy and security issues of data sharing in real-world scenarios. To address this, we propose an asynchronous FL data-sharing method, incorporating an interpretable reputation quantization mechanism based on graph convolutional networks. Data providers share data structures under differential privacy constraints, ensuring security while minimizing redundancy. We utilize deep reinforcement learning to classify vehicles by reputation level, optimizing FL aggregation efficiency. Experimental results show that the proposed scheme not only strengthens the security of trajectory prediction but also improves prediction accuracy.