分析分裂联合学习中的漏洞:评估对数据中毒攻击的强度
Aysha-Thahsin Zahir-Ismail1, Raj Shukla2
1Computing and Information Science, Anglia Ruskin University, Cambridge, UK.
Scientific reports
|August 19, 2025
概括
这项研究分析了对SFL (分裂联合学习) 的数据中毒攻击,这是一个保护隐私的机器学习方法. 基于距离和非目标的攻击显著降低了分类器的性能,远远超过了目标攻击.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 集中式机器学习面临隐私挑战.
- 分布式协作机器学习 (DCML) 提供了保护隐私的替代方案.
- 分拆联合学习 (SFL) 整合了分拆学习 (SL) 和联合学习 (FL).
研究的目的:
- 综合研究和分析数据中毒攻击对分割联合学习 (SFL) 的影响.
- 提出和评估针对SFL系统的新型攻击策略.
主要方法:
- 开发了三种数据中毒攻击策略:无目标,有目标和基于距离的.
- 对心电图信号分类和MNIST数据集的评估攻击.
- 不同的恶意客户端百分比和模型分层.
主要成果:
- 基于距离和非目标的攻击对分类器性能下降的影响更大.
- 针对不同的恶意客户端比率和分点,分析了攻击的有效性.
- 与基于距离和非目标策略相比,有针对性的攻击的影响较小.
结论:
- 分拆联合学习易受各种数据中毒攻击的影响.
- 基于距离和非目标的攻击对SFL分类器的完整性构成重大威胁.
- 了解这些漏洞对于开发强大的SFL防御至关重要.
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