LFighter:在联合学习中防御标签翻转攻击
Najeeb Moharram Jebreel1, Josep Domingo-Ferrer1, David Sánchez1
1Universitat Rovira i Virgili, Department of Computer Engineering and Mathematics, CYBERCAT Center for Cybersecurity Research of Catalonia, UNESCO Chair in Data Privacy, Av. Països Catalans 26, E-43007 Tarragona, Catalonia.
概括
联合学习 (FL) 易受标签翻转 (LF) 攻击. 我们介绍了LFighter,这是一种新的防御,通过分析模型参数梯度来检测和过恶意更新,从而提高全球模型的性能和准确性.
科学领域:
- 机器学习 机器学习
- 网络安全 网络安全
- 分布式系统 分布式系统
背景情况:
- 联合学习 (FL) 能够在保护数据隐私的同时实现协作模式培训.
- 恶意参与者可以通过毒性攻击来破坏FL模型,例如标签翻转 (LF).
- 现有的LF防御在数据分布假设和高维模型方面存在局限性.
研究的目的:
- 在联合学习中调查标签翻转攻击的行为.
- 提出一种新的防御机制,LFighter,对抗标签翻转攻击.
- 评估与现有防御相比,LFighter的有效性和优势.
主要方法:
- 研究了LF攻击对源类和目标类参数梯度的影响.
- 开发了LFighter以动态提取,集群和分析本地更新的梯度.
- 在全球模型聚合之前过掉恶意更新.
主要成果:
- 通过利用渐变特征,LFighter有效地检测到标签翻转攻击.
- 防御在不同的数据分布和模型维度上表现出稳健性.
- 在精度,错误率和稳定性方面,LFighter显著超过了最先进的防御系统.
结论:
- 参数梯度为在FL中检测标签翻转攻击提供了区分特征.
- 对于标签翻转攻击,LFighter提供了一种有效而强大的防御.
- 拟议的方法提高了联合学习系统的安全性和可靠性.
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