  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.
