Federated Learning with Differential Privacy: Enhancing Privacy in Decentralized Model Training

Abstract

Federated learning (FL) presents a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, the inherent collaborative nature of FL still poses privacy risks. This paper investigates the integration of differential privacy (DP) into the FL framework to provide an enhanced level of privacy protection. We introduce a novel approach where noise is injected into model parameters during the training process, adhering to the principles of DP. This mechanism mitigates the risk of information leakage from individual user data while maintaining model accuracy. We formally define the proposed algorithm and analyze its privacy-utility trade-off. The results demonstrate that the incorporation of DP significantly strengthens the privacy guarantees of FL, offering a robust solution for privacy-sensitive applications. Our work contributes to the growing body of research focused on secure and privacy-preserving FL techniques.