DA-PFL:在阶级不平衡下的个性化联合学习中的动态亲和聚合
IEEE transactions on neural networks and learning systems
|September 3, 2025
概括
这项研究引入了基于亲密关系的个性化联合学习 (PFL) 模式,以解决阶级不平衡问题. 这种新的方法提高了单个客户在联合学习场景中的模型准确性.
科学领域:
- 人工智能
- 机器学习
- 分布式系统
背景情况:
- 个性化联合学习 (PFL) 旨在为每个客户创建量身定制的模型.
- 目前的PFL方法经常汇总具有相似数据分布的客户.
- 这种基于相似性的聚合可能会加剧数据集中的类不平衡问题.
研究的目的:
- 提出一个新的基于动态亲和力的PFL (DA-PFL) 模型.
- 为了缓解联合学习中固有的阶级不平衡问题.
- 提高个性化学习模型的性能.
主要方法:
- 开发了一个补充的亲和度量指标来指导客户聚合.
- 实施了动态聚合策略,每轮调整客户选择.
- 在四个现实数据集上评估了DA-PFL模型.
主要成果:
- DA-PFL模型显著提高了客户端的准确性.
- 有效地减少了联合学习过程中阶级不平衡的负面影响.
- 超过现有的最先进的比较方法.
结论:
- 提出的DA-PFL模型为PFL的阶级失衡提供了有效的解决方案.
- 基于亲和力的动态聚合增强了个性化的模型性能.
- DA-PFL在各种数据集中显示出卓越的准确性.
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