Decentralized Federated Optimization with Byzantine Fault Tolerance via Blockchain

Abstract

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, the inherent vulnerability of FL to malicious participants, known as Byzantine attacks, poses a significant threat to the integrity and convergence of the learning process. This paper proposes a novel framework for decentralized federated optimization with Byzantine fault tolerance leveraging blockchain technology. Our approach utilizes a blockchain network to establish a tamper-proof record of model updates, ensuring data integrity and enabling robust aggregation even in the presence of malicious actors. The core mechanism involves a distributed consensus algorithm on the blockchain to validate and aggregate model updates, guaranteeing Byzantine fault tolerance. We demonstrate the feasibility and effectiveness of this approach, providing a secure and reliable foundation for decentralized FL. The key contributions include a blockchain-based architecture for FL, a Byzantine fault tolerance mechanism, and a system for ensuring model integrity. The proposed system addresses the critical challenge of security in FL, offering a pathway toward trustworthy and resilient distributed learning.