Decentralized Federated Learning with Byzantine Fault Tolerance using Blockchain-Based Verification

Abstract

Federated learning (FL) offers a promising approach to training machine learning models on decentralized datasets without directly sharing the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants can inject poisoned data or manipulate model updates, ultimately compromising the global model's integrity. This paper proposes a novel decentralized federated learning system incorporating Byzantine fault tolerance (BFT) achieved through a blockchain-based verification layer. The system leverages blockchain technology to cryptographically verify model updates from each participant before they are aggregated into the global model. This ensures data integrity and enables the detection and mitigation of Byzantine attacks. The core claim is that existing FL systems are vulnerable to these attacks. The proposed mechanism implements a decentralized FL system utilizing a blockchain-based verification layer. This represents a new approach to robust FL training. The system utilizes the following key components: participant nodes, a blockchain network, and a global model aggregator. The blockchain network is composed of multiple nodes that maintain a ledger of all model updates. The global model aggregator utilizes the blockchain network to verify model updates before aggregating them into the global model. The proposed system enhances the security and robustness of FL training by providing a tamper-proof audit trail and enabling the detection of malicious participants.