Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain
This work addresses data quality and trust issues in autonomous driving applications, representing an incremental improvement in federated learning methods.
The paper tackles the problem of mistrust in federated learning for trajectory prediction by proposing a reputation-driven asynchronous method with blockchain and differential privacy, resulting in enhanced security and improved prediction accuracy.
Federated learning combined with blockchain empowers secure data sharing in autonomous driving applications. Nevertheless, with the increasing granularity and complexity of vehicle-generated data, the lack of data quality audits raises concerns about multi-party mistrust in trajectory prediction tasks. In response, this paper proposes an asynchronous federated learning data sharing method based on an interpretable reputation quantization mechanism utilizing graph neural network tools. Data providers share data structures under differential privacy constraints to ensure security while reducing redundant data. We implement deep reinforcement learning to categorize vehicles by reputation level, which optimizes the aggregation efficiency of federated learning. Experimental results demonstrate that the proposed data sharing scheme not only reinforces the security of the trajectory prediction task but also enhances prediction accuracy.