联邦学习中后门攻击的检测与防御方法

Chen Zhao, Yu Wen, Shuailou Li, Fucheng Liu, Dan Meng
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引用次数: 14

摘要

联邦学习是为了在机器学习模型训练中保护数据隐私和安全而提出的一种安全的机器学习技术。然而,最近的研究表明,联邦学习容易受到后门攻击,如模型替换攻击和分布式后门攻击。大多数后门防御技术都不适合联邦学习,因为它们是基于整个数据样本,在联邦学习场景中无法保存。新提出的联邦学习方法牺牲了模型的准确性,并且在多次训练中攻击持续存在时仍然失败。在本文中,我们提出了一种新的有效的检测和防御技术,称为FederatedReverse用于联邦学习。我们对我们的解决方案进行了广泛的实验评估。实验结果表明,与现有技术相比,我们的解决方案可以有效地检测和防御联邦学习中的各种后门攻击,大大降低了后门攻击的成功率和持续时间,训练模型的准确率几乎不降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FederatedReverse: A Detection and Defense Method Against Backdoor Attacks in Federated Learning
Federated learning is a secure machine learning technology proposed to protect data privacy and security in machine learning model training. However, recent studies show that federated learning is vulnerable to backdoor attacks, such as model replacement attacks and distributed backdoor attacks. Most backdoor defense techniques are not appropriate for federated learning since they are based on entire data samples that cannot be hold in federated learning scenarios. The newly proposed methods for federated learning sacrifice the accuracy of models and still fail once attacks persist in many training rounds. In this paper, we propose a novel and effective detection and defense technique called FederatedReverse for federated learning. We conduct extensive experimental evaluation of our solution. The experimental results show that, compared with the existing techniques, our solution can effectively detect and defend against various backdoor attacks in federated learning, where the success rate and duration of backdoor attacks can be greatly reduced and the accuracies of trained models are almost not reduced.
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