Distributed Federated Learning with Differential Privacy for Personalized Models

Abstract

Federated learning (FL) presents a promising approach to training machine learning models across decentralized devices while preserving data privacy. However, traditional FL methods often lack sufficient privacy guarantees, leaving users vulnerable to privacy breaches. This work proposes a novel framework for distributed federated learning incorporating differential privacy (DP). Our approach enhances the standard FL process by adding noise to the model updates, effectively masking individual user contributions. This guarantees a rigorous privacy budget, quantified by the ε and δ parameters of the DP mechanism. The resulting system enables personalized model training – adapting the global model to individual user data – while maintaining strong privacy protections. We outline the core components of the system, including client selection, local model training with DP noise injection, and aggregation of noisy updates. The theoretical analysis demonstrates the effectiveness of the proposed method in achieving strong privacy guarantees alongside reasonable model accuracy. The presented framework provides a viable path for building trust-worthy and privacy-preserving collaborative learning systems.