在联合学习中,针对多标签中毒攻击的防御方法
Wei Ma1, Qihang Zhao2, Wenjun Tian2
1School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450045, China. mawei@ncwu.edu.cn.
Scientific reports
|July 18, 2025
概括
这项研究介绍了在联合学习中对多标签翻转攻击的新防御方法. 该方法有效地识别和删除恶意更新,增强模型安全性和隐私.
科学领域:
- 机器学习 机器学习
- 网络安全 网络安全
- 数据 隐私 数据 隐私 数据
背景情况:
- 联合学习 (FL) 允许在不共享原始数据的情况下进行协作模式培训,从而增强隐私.
- FL系统容易受到数据中毒攻击,如标签翻转,损害模型完整性.
- 现有的防御系统在与复杂的多标签翻转攻击作斗争.
研究的目的:
- 建议在联合学习中针对多标签翻转攻击提供强有力的防御机制.
- 加强联合学习模型的安全性和可靠性,以防止复杂的对抗性操纵.
主要方法:
- 从输出层神经元中提取梯度.
- 应用集群分析,使用一组指标来区分良性和恶意参与者.
- 识别和过出恶意模型更新.
主要成果:
- 提出的方法有效地识别和过恶意更新.
- 对多标签翻转攻击表现出强大的稳定性,即使攻击者的比例很高.
- 在各种数据集 (MNIST,FashionMNIST,NSL-KDD,CICIDS-2017) 中,在准确性和稳定性方面优于现有的防御方法.
结论:
- 开发的防御方法在保护联合学习免受先进的中毒攻击方面提供了显著的改进.
- 该方法提供了一种可靠的解决方案,用于在保护隐私的分布式学习环境中保持模型完整性.
- 对各种攻击场景和高百分比的恶意参与者有效.
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