ICAFS:用于垂直联合学习的客户间识别功能选择
Ruochen Jin1,2, Boning Tong1, Shu Yang1
1University of Pennsylvania, Philadelphia, PA, USA.
IEEE transactions on artificial intelligence
|February 13, 2026
概括
本研究介绍了ICAFS,这是一个用于垂直联合学习 (VFL) 的特征选择的新方法. 通过考虑客户间特征交互,ICAFS提高了模型准确性,优于现有的方法.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 垂直联合学习 (VFL) 能够在跨客户端分区的数据上进行协作模型培训.
- 功能选择 (FS) 在VFL中至关重要,因为在客户端中分布式,独特的功能子集.
- 在VFL中现有的FS方法往往忽略了关键的客户间特征交互,限制了模型性能.
研究的目的:
- 为垂直联合学习 (VFL) 开发一种有效的特征选择 (FS) 方法,以考虑客户间特征交互.
- 引入一种名为ICAFS的多阶段组合方法,用于在VFL中增强FS.
- 通过解决客户内部专注FS的局限性,提高VFL的预测准确性.
主要方法:
- 引入了ICAFS,这是一个多阶段的组合方法,用于VFL的特征选择.
- 采用了条件特征合成和多个可学习特征选择器.
- 使用合成嵌入的方便集体FS,绕过私人梯度共享.
主要成果:
- 与最先进的方法相比,ICAFS在预测准确度方面表现优越.
- 在多个现实世界数据集上的实验验验证了拟议方法的有效性.
- 该方法使模型训练能够使用从真实数据中获得的精细嵌入式.
结论:
- 通过考虑客户间的功能交互,ICAFS为垂直联合学习中的功能选择提供了有效的解决方案.
- 拟议的方法通过私人梯度共享来提高模型性能,而不会损害数据隐私.
- 通过复杂的特征选择,ICAFS代表了VFL的重大进步,提高了预测准确度.
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