杆变化图表表示模型中毒在联合学习上的杆变化图表表示
概括
一个新的联合学习 (FL) 攻击,VGAE-MP,使用对抗性的变量图自编码器来毒害没有训练数据的模型. 这种有效的,难以捉摸的攻击绕过了当前的防御,威胁到FL系统的完整性.
科学领域:
- 人工智能的人工智能
- 机器学习安全 机器学习安全
- 联邦学习学习 (Federated Learning) 是一种学习方式.
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型培训.
- 模型中毒 (MP) 攻击通过操纵本地模型来威胁FL的完整性.
- 现有的攻击通常需要访问训练数据或是可检测的.
研究的目的:
- 引入一种新的训练数据-无绑定模型中毒攻击联合学习.
- 开发一种既有效又难以检测的方法.
- 评估这种新攻击对FL系统构成的威胁.
主要方法:
- 扩展了一个对抗变量图自编码器 (VGAE) 用于模型中毒.
- 开发了VGAE-MP攻击,利用无人听到的良性本地模型,而没有训练数据访问.
- 实现了使用VGAE和子梯度下降进行最佳良性模型选择的新攻击算法.
主要成果:
- VGAE-MP攻击有效地降低了联合学习的准确性.
- 现有的防御机制在检测VGAE-MP攻击时证明是无效的.
- 这次袭击表明,对FL的安全和可靠性构成严重威胁.
结论:
- 拟议的VGAE-MP攻击在联合学习安全威胁方面取得了重大进展.
- VGAE-MP的数据无性和难以捉摸性突出了当前FL防御中的漏洞.
- 对此类复杂攻击的强有力的防御策略的进一步研究至关重要.
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