Decentralized Federated Learning with Byzantine Fault Tolerance

Abstract

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly sharing the data itself. However, this decentralized nature introduces significant vulnerabilities. Malicious participants, known as Byzantine nodes, can inject biased or corrupted models into the training process, compromising the overall model accuracy and potentially introducing harmful biases. This paper proposes a novel decentralized federated learning framework incorporating Byzantine Fault Tolerance (BFT) mechanisms. The core claim is that a BFT-enabled decentralized FL system provides robustness against malicious actors, guaranteeing model convergence even when some nodes are compromised. We outline the system architecture, detailing the BFT protocol integration, aggregation strategies, and communication protocols. The proposed system leverages a verifiable distributed consensus mechanism, allowing for the detection and mitigation of Byzantine behavior. The theoretical analysis demonstrates the system's resilience to arbitrary Byzantine failures and provides a framework for quantifying the impact of malicious participation. This work represents a significant advancement in securing FL deployments, fostering trust and reliability in collaborative learning environments.