Distributed Federated Learning with Differential Privacy for Collaborative Graph Analytics

Abstract

Federated learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data sources without directly exchanging the raw data. However, the application of FL to graph analytics, particularly when dealing with sensitive graph data, presents significant challenges due to the inherent privacy risks associated with sharing graph structures and node attributes. This paper proposes a novel distributed federated learning framework incorporating differential privacy (DP) to address these challenges. The framework leverages secure aggregation techniques to minimize information leakage during model aggregation and integrates local differential privacy mechanisms at the node level to provide robust privacy guarantees. We demonstrate the feasibility and effectiveness of this approach through a theoretical analysis and conceptual design, highlighting its potential to enable collaborative graph analytics while preserving the privacy of participating nodes. The key contributions of this work include a tailored FL architecture for graph data, the integration of DP for enhanced privacy, and a discussion of the trade-offs involved in balancing privacy and model accuracy.