Privacy-Enhanced Federated Learning for Distributed Heterogeneous Data

This paper proposes a heterogeneous federated learning method for collaborative modeling, addressing data heterogeneity and privacy protection in multi-center scenarios. Based on the traditional federated learning framework, the method introduces a regularization term to mitigate performance instability caused by inconsistent data distributions across clients. It also designs a weighted aggregation strategy based on dynamic client features to enhance model convergence and robustness in multi-source heterogeneous environments. To ensure data privacy, the method applies differential privacy during local training. Noise is added to the model updates to prevent leakage of sensitive information. During development, the method is evaluated using a real federated learning dataset. It is compared with several mainstream approaches to validate its advantages in key performance metrics such as Accuracy, AUC, and F1-Score. Experimental results show that the proposed method effectively balances the personalized needs of local models with the goal of global consistency. It adapts to differences across clients and achieves better federated modeling performance. The overall architecture demonstrates strong practicality, security, and generalization ability. It is well-suited for real-world applications where centralized data storage is not feasible.