Distributed Differential Privacy with Federated Learning using Byzantine Fault Tolerance

Abstract

This paper presents a novel approach to achieving robust differential privacy within federated learning systems, specifically designed to mitigate the risks posed by Byzantine attacks. Traditional federated learning methods frequently struggle to provide rigorous privacy guarantees when confronted with malicious participants who intentionally distort model updates. Our method integrates a Byzantine fault-tolerant consensus mechanism with adaptive noise injection, leveraging participant trustworthiness scores to dynamically adjust privacy protection. The key innovation lies in the proactive assessment of participant reliability and the subsequent tailoring of noise parameters, resulting in a significantly more resilient privacy framework. We demonstrate the effectiveness of this approach through a theoretical analysis and outline its potential for deployment in scenarios where data heterogeneity and adversarial behavior are significant concerns. The core claim is achieving robust differential privacy guarantees in federated learning scenarios, specifically addressing the vulnerability to Byzantine attacks where malicious participants attempt to compromise privacy. The core mechanism employs a Byzantine fault-tolerant consensus mechanism within the federated learning framework, combined with adaptive noise injection based on participant trustworthiness scores derived from local model variations.