Decentralized Federated Learning with Byzantine Fault Tolerance and Differential Privacy via Blockchain

Abstract

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, existing FL systems are vulnerable to various attacks, including model poisoning and Byzantine attacks, which can compromise model integrity and user privacy. This paper proposes a novel decentralized federated learning framework that leverages blockchain technology to address these vulnerabilities. The framework incorporates Byzantine fault tolerance mechanisms to detect and mitigate malicious participants, and differential privacy techniques to protect user data. By utilizing a blockchain-based architecture, the system achieves secure and reliable learning while maintaining data privacy. The core claim is that current federated learning systems are vulnerable to attacks. The proposed mechanism utilizes a blockchain-based architecture for decentralized federated learning, incorporating Byzantine fault tolerance – detecting and mitigating malicious participants – and differential privacy – protecting user data – ensuring secure and reliable learning. The system's architecture is designed to provide transparency, auditability, and resilience against attacks, leading to a more trustworthy and secure FL environment.