基于聚类分析的横向联邦学习方案

A Horizontal Federated Learning Scheme Based on Cluster Analysis

联邦学习是一种分布式的学习方法,参与者协同训练模型,参与者将数据保留在本地,只是把模型参数发送到服务器,从而保证了数据的安全性。研究发现,在模型训练的过程中,存在遭受数据投毒的数据或恶意窜改的数据,使训练的模型难以取得较好的预测效果。因此,文章提出一个基于聚类分析的参与者评价算法,通过对数据集进行联合分析并采取相应的措施来防御投毒攻击。实验结果证明了方案的合理性和实效性,有效防止了横向联邦学习中的投毒攻击。

Federated learning is a distributed learning method. Participants cooperate to train the models, keep the data locally,and only send the model parameters to the service to ensure the security of the data. It is found that in the process of model training, there are data poisoned by data or maliciously tampered data, which makes it difficult for the trained model to achieve better prediction results.Therefore, this paper proposes a participant evaluation algorithm based on cluster analysis, through the joint analysis of data sets and taking corresponding measures to prevent poisoning attacks. The experimental results show that the scheme is reasonable and effective, and effectively prevent poisoning attacks in horizontal federated learning.