Distributed Federated Learning with Byzantine Fault Tolerance via Blockchain Verification

Abstract

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, FL systems are vulnerable to Byzantine attacks, where malicious participants can inject faulty model updates, compromising model accuracy and potentially causing significant harm. This paper proposes a novel framework for distributed federated learning with Byzantine fault tolerance, utilizing a blockchain-based verification layer. Our system employs a distributed consensus mechanism on a blockchain to validate model updates generated by participating nodes. This approach provides a robust, trustless environment, guaranteeing data integrity and ensuring model accuracy even in the presence of malicious actors. The core claim is the implementation of FL systems with Byzantine fault tolerance through blockchain verification. The core mechanism involves local model training followed by blockchain recording of updates and distributed consensus verification. This work represents a significant advancement by integrating the benefits of FL with the security and immutability offered by blockchain technology. We demonstrate a viable path toward building resilient and reliable FL systems for diverse applications.