VFL-Cafe:通过动态缓存和特征选择进行沟通高效的垂直联合学习
Jiahui Zhou1, Han Liang1, Tian Wu1
1School of Computer and Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China.
Entropy (Basel, Switzerland)
|January 24, 2025
概括
垂直联合学习 (VFL) 使用动态缓存和特征选择来降低通信成本并提高模型准确性. VFL-Cafe提高了效率而不牺牲性能,即使有噪音数据.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 垂直联合学习 (VFL) 实现了维护隐私的协作模式培训.
- 高通讯成本是VFL的一个主要挑战,因为结果的传输是中间的.
- 使用过时结果的现有方法可以降低准确性,特别是在有噪音数据的情况下.
研究的目的:
- 提出VFL-Cafe,一种新的VFL培训方法.
- 提高VFL的通信效率和模型准确性.
- 解决目前通信效率高的VFL方法的局限性.
主要方法:
- 动态缓存中间结果以进行战略重用.
- 功能选择集成到本地更新中以减轻噪音功能.
- 缓存配置优化的理论分析.
主要成果:
- VFL-Cafe显著降低了通讯上空成本.
- 该方法保持或提高模型的准确性.
- 实验结果验证了VFL-Cafe的疗效.
结论:
- VFL-Cafe提供了一种有效的解决方案,以实现通信效率高的VFL.
- 动态缓存和功能选择是提高性能的关键.
- 拟议的方法平衡了VFL培训中的效率和准确性.
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