费德勒:当联合学习与有噪音标签的学习相遇时
Ruizhi Pu1, Lixing Yu2, Shaojie Zhan2
1Western University, Department of Computer Science, London, N6A 5B7, Ontario, Canada.
概括
联合学习 (FL) 与杂的标签扎,导致业绩下降. 这项研究揭示了早期培训阶段在客户之间存在差异,导致记住不正确的数据. 将这些阶段与FedELR对齐可以提高模型的稳定性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 分布式系统 分布式系统
背景情况:
- 联合学习 (FL) 通常假定高质量的培训标签,这在现实应用中往往不是这样.
- 在FL的噪音标签导致显著的性能下降,影响医疗图像分类等关键领域的服务质量.
研究的目的:
- 通过分析早期培训现象 (ETP),研究噪音标签对联合学习的影响.
- 在杂的FL场景中确定性能下降的根本原因.
- 提出一个新的框架,以减轻在佛罗里达州噪音标签的影响.
主要方法:
- 分析FL在当地和全球模型中早期培训阶段的动态.
- 鉴定早期培训阶段的不一致性,原因是客户之间的不同噪音类.
- 提出FedELR,一个使用早期学习规范化 (ELR) 的框架,以调整当地模型培训阶段.
主要成果:
- 证明了早期培训阶段在当地客户之间以及在杂的FL中在当地和全球模型之间存在差异.
- 显示当地客户倾向于在全球模型达到最佳性之前记住噪音标签.
- 通过广泛的实验验验证了FedELR在调整早期培训阶段和提高模型稳定性方面的有效性.
结论:
- 早期培训现象 (ETP) 对于理解杂的联合学习至关重要.
- 将早期培训阶段与本地模型对齐,是强大的噪音FL的必要原则.
- 在有噪音标签的情况下,FedELR提供了一种简单而有效的解决方案,以提高联合学习的性能.
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