FedID:通过动态识别增强联合学习安全性
概括
联合学习 (FL) 面临着后门攻击. 一个新的防御,FedID,使用多种指标进行自适应检测,优于现有的方法对隐形和自适应威胁,即使使用非IID数据.
科学领域:
- 机器学习 机器学习
- 网络安全 网络安全
- 分散式系统 分散式系统 分散式系统
背景情况:
- 联合学习 (FL) 提供隐私,但容易受到后门攻击.
- 目前的防御系统在与非IID数据或类似梯度的隐形攻击作斗争.
研究的目的:
- 在佛罗里达州开发强大的防御系统,以抵御佛罗里达州的各种后门攻击.
- 解决高维度和非IID设置中现有的基于距离的防御的局限性.
主要方法:
- 提出FedID,一种使用多个指标的策略,用于后门检测,具有动态权重.
- 引入一个修改的z-score,用于在聚合过程中选择梯度.
- 对各种攻击和非IID数据分布进行FedID评估.
主要成果:
- FedID有效地检测到各种恶意梯度,超过以前的防御.
- 对旨在绕过防御的适应性攻击表现出强度.
- 在各种非IID数据分布中,在良性任务上保持高性能.
结论:
- 费迪德提供了一个简单,有效和适应性的解决方案,用于后门防御在佛罗里达州.
- 多度方法克服了基于单度和欧几里德距离的方法的局限性.
- FedID对适应性攻击具有弹性,并且在非IID条件下表现良好.
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