MMVFL:一个简单的垂直联合学习框架,用于多类多参与者场景
Siwei Feng1, Han Yu2, Yuebing Zhu1
1School of Computer Science & Technology, Soochow University, Suzhou 215000, China.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
本研究介绍了一个多参与者多类垂直联合学习 (MMVFL) 框架. 在联合学习场景中,MMVFL可实现安全的标签共享,以提高多类分类性能.
科学领域:
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
- 分布式计算 (Distributed Computing) 是一种分布式计算.
背景情况:
- 联合学习 (FL) 能够在保护数据隐私的同时实现协作模式培训.
- 垂直联合学习 (VFL) 解决了具有共享样本但具有独特特征的场景,其中一方持有标签.
- 现有的VFL研究主要集中在双边,二进制类问题上,最近的工作强调通信和安全.
研究的目的:
- 提出一个新的框架,多参与者多类垂直联合学习 (MMVFL),用于多类VFL问题与多个参与者.
- 为了使标签所有者能够将维护隐私的标签共享给VFL环境中的其他参与者.
- 证明MMVFL的有效性,特别是当与特征选择集成时,用于多类分类任务.
主要方法:
- MMVFL框架扩展了多视图学习 (MVL) 原则,以促进多个VFL参与者之间安全的标签共享.
- 在MMVFL中集成了一个特征选择方案,以评估其性能和量化特征的重要性.
- 该框架允许测量个人参与者对集体模型的贡献.
主要成果:
- MMVFL有效地在多个VFL参与者之间以保护隐私的方式共享标签信息.
- 在MMVFL中集成的特征选择显示了与现有方法相比的多类分类性能.
- 该框架成功量化了特征的重要性和参与者的贡献,验证了其实用性.
结论:
- 拟议的MMVFL框架是多类垂直联合学习的可行解决方案,涉及多方.
- 在保持隐私的同时,MMVFL促进了有效的标签共享,并实现了具有竞争力的分类性能.
- 该框架的模块化设计使其易于与先进的通信和安全技术集成.
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