Decentralized Federated Learning with Byzantine Fault Tolerance via Differential Privacy

Abstract

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly sharing the data itself. However, existing FL systems face significant vulnerabilities, including malicious participant attacks and data breaches. This paper proposes a novel decentralized federated learning framework that integrates Byzantine fault tolerance (BFT) protocols, inspired by blockchain technology, with differential privacy (DP) mechanisms. The core claim is that this synergistic combination provides a substantially more robust and secure FL paradigm. Specifically, the system utilizes BFT to detect and mitigate malicious behavior from participants, ensuring data integrity and model robustness, while DP safeguards user privacy by adding controlled noise to the model updates. This framework allows the system to tolerate a certain number of Byzantine failures while maintaining accurate models. The system architecture is decentralized, eliminating a single point of failure and enhancing overall resilience. We demonstrate the potential of this approach through a theoretical analysis and outline a conceptual implementation strategy.