BRFL: A blockchain-based byzantine-robust federated learning model

【Author】 Li, Yang; Xia, Chunhe; Li, Chang; Wang, Tianbo

【Source】JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING

【影响因子】4.542

【Abstract】With the increasing importance of machine learning, the privacy and security of training data have become a concern. Federated learning, which stores data in distributed nodes and shares only model parameters, has gained significant attention for addressing this concern. However, a challenge arises in federated learning due to the byzantine attack problem, where malicious local models can compromise the global model's performance during aggregation. This article proposes the Blockchain-based Byzantine-Robust Federated Learning (BRFL) model, which combines federated learning with blockchain technology. We improve the robustness of federated learning by proposing a new consensus algorithm and aggregation algorithm for blockchain-based federated learning. Meanwhile, we modify the block saving rules of the blockchain to reduce the storage pressure of the nodes. Experimental results on public datasets demonstrate the superior byzantine robustness of our secure aggregation algorithm compared to other baseline aggregation methods, and reduce the storage pressure of the blockchain nodes.

【Keywords】Federated learning; Blockchain; Pearson correlation coefficient; Spectral clustering

【发表时间】2025 FEB

【收录时间】2024-10-31

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-联邦学习