Decentralized Machine Learning via Federated Learning with Byzantine Fault Tolerance

Abstract

Federated learning (FL) offers a promising approach to distributed machine learning, enabling model training on decentralized devices without directly exchanging sensitive data. However, the inherent vulnerability of FL systems to Byzantine failures – where malicious participants intentionally corrupt the learning process – poses a significant threat to its reliability and security. This paper proposes a decentralized machine learning framework leveraging federated learning with Byzantine fault tolerance (BFT). The core claim is the development of a robust FL system capable of maintaining accurate and secure model training despite the presence of adversarial actors. The proposed mechanism utilizes verifiable computation and secret sharing techniques to mitigate the impact of Byzantine attacks. This work addresses a critical gap in existing FL research by explicitly tackling the security challenges associated with adversarial behavior, paving the way for more trustworthy and resilient distributed learning applications. The system is designed to ensure data privacy and model integrity, even when compromised by malicious nodes. The theoretical framework and the proposed architecture will be discussed in detail.