DefendFL:一个保护隐私的联合学习计划,以防止毒害攻击
IEEE transactions on neural networks and learning systems
|August 1, 2024
概括
DefendFL通过引入基于面具的框架来增强联合学习 (FL) 的安全性,该框架可以保护梯度隐私,并有效地检测和减轻毒害攻击,改善模型完整性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 联合学习 (FL) 允许在没有直接数据共享的情况下进行协作模式培训,但面临隐私泄露和中毒攻击的脆弱性.
- 基于面具的FL框架提供了效率和功能,但容易受到复杂的中毒攻击.
- 现有的方法缺乏实际的检测机制,以面具为基础的FL中毒.
研究的目的:
- 提出 DefendFL,一个高效的,保护隐私的,可检测中毒的基于面具的 FL 计划.
- 为了解决面具式FL对毒药攻击的脆弱性.
- 提供实用手段,以检测和减轻FL的恶意活动.
主要方法:
- 使用一个对线性面具来保护梯度隐私.
- 在检测恶意掩盖梯度时采用了同位数相似性.
- 实施了面具验证机制,以确保聚合有效性并防止面具操纵.
- 通过在聚合过程中去除或减轻恶意梯度来抵抗中毒.
主要成果:
- DefendFL有效地检测和减轻面具式FL中毒攻击.
- 与现有的保护隐私的检测方法相比,拟议的方案显示出更高的效率.
- 安全分析和实验评估验证了DefendFL的有效性.
结论:
- DefendFL提供了一个强大的解决方案,用于增强基于面具的联合学习的安全性.
- 该框架成功地平衡了隐私保护与有效防御毒害攻击.
- 在保护协作机器学习环境方面,DefendFL是一个重大进步.
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