Design of Financial Data Sharing Algorithm Based on Differential Privacy and GAN Heterogeneous Optimization under the Federated Learning Framework

Against the backdrop of financial data sharing, although federated learning can prevent the direct exposure of raw data, it still faces challenges such as device performance differences, data distribution imbalance, and privacy leakage risks.This paper proposes an optimization framework that integrates differential privacy and generative adversarial networks.During local training, it transmits effective information by generating alternative samples and feature distributions that comply with privacy constraints.In the communication process, it uses hierarchical scheduling and dynamic detection mechanisms to reduce congestion and invalid waiting.In the model aggregation stage, it dynamically adjusts weights through multi-dimensional contribution metrics to alleviate convergence shift.The experimental results show that this method significantly reduces the communication burden and improves robustness while ensuring accuracy.The final constructed prototype system realizes the complete functions from client management to global scheduling and security control, providing a feasible solution for achieving efficient sharing and collaborative modeling in the financial field under privacy protection.