Decentralized Federated Optimization with Byzantine Fault Tolerance using Blockchain

Abstract

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging the data itself. However, current FL frameworks are susceptible to attacks from malicious participants, known as Byzantine attacks, which can compromise model integrity and potentially inject biases. This paper proposes a novel decentralized federated optimization system leveraging blockchain technology and Byzantine Fault Tolerance (BFT) consensus algorithms to mitigate these vulnerabilities. Our system allows participants to securely contribute model updates and verify their correctness, ensuring a robust and resilient optimization process. The core innovation lies in integrating blockchain's immutability and cryptographic capabilities with BFT to create a system capable of detecting and rejecting malicious updates, thereby safeguarding the global model from manipulation. This approach provides a fundamentally more secure and trustworthy FL environment compared to traditional centralized or even existing decentralized FL methods. We explore the theoretical foundations of this system and outline its key components, focusing on the BFT consensus mechanism and its integration with the FL optimization process. The system's design emphasizes transparency, auditability, and resistance to adversarial attacks, representing a significant advancement in the field of secure and reliable distributed machine learning.