BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model
Yang Li Chunhe Xia Chang Li Tianbo Wang
With the increasing importance of machine learning, the privacy and security
of training data have become critical. 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 (BRLF) model
that combines federated learning with blockchain technology. This integration
enables traceability of malicious models and provides incentives for locally
trained clients. Our approach involves selecting the aggregation node based on
Pearson's correlation coefficient, and we perform spectral clustering and
calculate the average gradient within each cluster, validating its accuracy
using local dataset of the aggregation nodes. Experimental results on public
datasets demonstrate the superior byzantine robustness of our secure
aggregation algorithm compared to other baseline byzantine robust aggregation
methods, and proved our proposed model effectiveness in addressing the resource
consumption problem.