Research on Adaptive Noise Mechanism for Differential Privacy Optimization in Federated Learning

This paper proposes an adaptive differential privacy mechanism for federated learning that optimizes the trade-off between model performance and privacy protection. The mechanism incorporates a dynamic noise generation algorithm that adjusts noise levels based on training states and gradient information, coupled with an efficient privacy budget allocation strategy. The proposed approach addresses the limitations of existing static noise addition methods by introducing a multi-factor adaptation framework that considers both local training characteristics and global model convergence states. The system architecture implements a dual-layer privacy protection scheme, combining adaptive noise injection at the client level with optimized privacy budget management at the server level. Experimental evaluation on multiple benchmark datasets, including MNIST and CIFAR-10, demonstrates that our approach performs better than existing methods. The results show a 3.5-5.8% improvement in model accuracy while maintaining equivalent privacy guarantees and a 25-30% reduction in communication overhead. Theoretical analysis establishes rigorous bounds on privacy protection and model convergence, providing formal guarantees for the proposed mechanism. The comprehensive evaluation validates the effectiveness of our approach across various operational scenarios and data distributions, making it particularly suitable for real-world applications with heterogeneous privacy requirements.