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.

翻译：随着机器学习的重要性日益增加，训练数据的隐私和安全变得至关重要。联邦学习将数据存储在分布式节点中，仅共享模型参数，因此在解决这一问题上受到广泛关注。然而，联邦学习中存在拜占庭攻击问题，即恶意局部模型可能在聚合过程中损害全局模型的性能。本文提出基于区块链的拜占庭鲁棒联邦学习（BRFL）模型，该模型将联邦学习与区块链技术相结合。这种整合能够追踪恶意模型并为本地训练客户端提供激励。我们的方法基于皮尔逊相关系数选择聚合节点，并执行谱聚类及计算每个聚类内的平均梯度，利用聚合节点的本地数据集验证其准确性。在公共数据集上的实验结果表明，与其它基线拜占庭鲁棒聚合方法相比，我们的安全聚合算法具有更优越的拜占庭鲁棒性，同时证明了所提模型在解决资源消耗问题上的有效性。