对抗性攻击对联合学习的影响:一项调查
IEEE transactions on pattern analysis and machine intelligence
|October 9, 2023
概括
联合学习 (FL) 容易受到对抗性攻击. 这项调查分析了攻击的影响,预算和可见性,为保护FL系统提供了洞察力.
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) 允许在分散的数据上进行模型培训.
- FL的分布式性质引入了对敌对攻击的脆弱性.
- 现有的调查往往忽略了攻击影响指标,如预算,可见性和通用性.
研究的目的:
- 为了提供一个全面的概述的对抗性攻击在FL.
- 分析攻击的影响,考虑预算,可见性和可概括性.
- 为了识别具有低预算,低可见性和高影响力的FL攻击.
主要方法:
- 关于FL攻击和防御的文献综述.
- 对攻击特征的分析:预算,可见性,概括性.
- 包含10个关于FL安全的案例研究.
主要成果:
- 联合学习容易受到各种对抗性攻击.
- 攻击的影响在很大程度上受到预算和可见性的影响.
- 确定了FL对抗防御的最新进展.
结论:
- 对攻击影响的更深入的理解对飞机安全至关重要.
- 专注于低预算,低可见性,高影响力的攻击至关重要.
- 未来的研究应该解决在保护FL系统中发现的挑战.
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