Application of Privacy Protected Federated Learning in Global Trade Flow Prediction and Anomaly Monitoring

Under the background of rigorous data privacy protection regulations and the challenge of "data silos" in cross-border trade collaborative analysis, this paper aims to establish a privacy preserving federated learning method. First of all, a federated network of cross-border participants is established and trained locally based on the Gated Recurrent Unit (GRU) model; Then, differential privacy technology is introduced on the client side, which can quantify the degree of privacy protection offered by privacy technology by gradient clipping and adding Laplacian noise; Thereafter, this article designs a dynamic weighted aggregation strategy based on attention mechanism to adaptively fuse the model update of heterogeneous data nodes; Finally, a global model is generated based on secure aggregation protocol, which is synchronously supported by traffic prediction and anomaly monitoring. The experimental results show that the framework can greatly improve the prediction accuracy of local model (MAE=15.8), the F1 score of anomaly detection is 0.882, and member inference attack success rate is reduced to 3.8%. It has been proved that the framework is feasible and superior in implementing cross-border trade collaborative intelligent analysis while protecting data privacy strictly.