样本级的原型联合学习
IEEE transactions on pattern analysis and machine intelligence
|September 19, 2025
概括
联合学习 (FL) 在分散数据上训练模型,但非IID数据是一个挑战. 样本级原型联合学习 (SL-PFL) 为每个数据样本提供细粒度的个性化,提高模型性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 联合学习 (FL) 能够在分散的数据上进行协作模式培训,解决隐私问题.
- 跨客户非相同且独立分布的 (非IID) 数据是FL的一个主要挑战,特别是在内部数据异质性的跨 silo 设置中.
- 现有的FL方法往往忽略了客户内部数据异质性,或者需要不可访问的域指标.
研究的目的:
- 提出一种新的联合学习方法,以应对客户内部非IID数据的挑战.
- 引入一个细粒度个性化方法,在个人数据样本级别调整模型.
- 开发一种联合学习解决方案,不需要明确的域指标.
主要方法:
- 引入了样本级原型联合学习 (SL-PFL),将原型学习整合到FL框架中.
- 开发了一种用于为每个数据样本创建个性化模型的方法,而不是为每个客户创建单个模型.
- 确保该方法可以在没有基础真理域标签的情况下得到有效的训练.
主要成果:
- 与使用全球或客户级别个性化模型的现有FL方法相比,SL-PFL表现出更高的性能.
- 提出的方法在各种现实世界的回归和分类任务中取得了最先进的结果.
- 有效性在各种领域得到验证,包括天气,计算机视觉和医疗保健.
结论:
- 在联合学习中,SL-PFL有效地解决了客户内部非IID数据挑战.
- 在联邦设置中,样品级别的个性化比全球或客户级别的个性化提供了显著的优势.
- 拟议的方法为现实世界中使用异质数据的联合学习应用提供了实际的解决方案.
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