Federated Learning with Differential Privacy: A Distributed Approach

Abstract

This paper presents a novel approach to distributed machine learning, termed Federated Learning with Differential Privacy (FLDP), which addresses the critical challenge of privacy preservation in collaborative model training. Traditional federated learning techniques, while mitigating the risks associated with centralized data collection, still expose model updates, potentially revealing sensitive information about the underlying data. FLDP overcomes this limitation by directly applying differential privacy to the model aggregation process. Specifically, we introduce a locally sensitive differential privacy mechanism that injects noise into each node's model update before aggregation. This ensures that the impact of any single node's contribution on the global model is bounded, effectively protecting individual data privacy. The proposed method allows for reliable model convergence while rigorously upholding privacy guarantees. We demonstrate the feasibility and effectiveness of FLDP through a theoretical analysis and outline a practical implementation strategy. The key contribution of this work lies in the shift from applying differential privacy solely to data training to directly incorporating it into the model aggregation phase, creating a more robust and privacy-preserving distributed learning system.