使用类和域信息来解决联邦学习中的域转移.
IEEE transactions on neural networks and learning systems
|February 6, 2026
概括
联合学习 (FL) 面临着由于异质数据的领域转移带来的挑战. 本研究引入了一种新的FL框架,该框架将域不变和域特定特征分离并学习,从而提高全球模型性能和稳定性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 分布式系统 分布式系统
背景情况:
- 联合学习 (FL) 受到异质客户端数据分布的挑战,导致领域转移.
- 这种域位转移导致了不同的本地模型,并降低了FL的整体全球表现.
- 现有的方法很难有效地处理FL的域变化的复杂性.
研究的目的:
- 提出一个新的联合学习框架,解决领域转移问题.
- 解和协作学习域不变和域特定表示.
- 增强全球模型对域变化的稳定性和性能.
主要方法:
- 引入了一个类和域意识的FL框架.
- 采用交叉门特征分离 (CGFS) 模块来解域和类特征.
- 使用异质原型对比学习 (HPCL) 进行特征可区分,并使用梯度重量化层次聚合 (GHA) 策略进行服务器聚合.
主要成果:
- 拟议的框架成功地解并学习了域不变和域特定的表示.
- 在两个具有域转移的FL数据集上的实验结果表明了卓越的性能.
- 该方法在处理域变异方面始终优于现有的最先进的方法.
结论:
- 拟议的类和域意识的FL框架有效地减轻了域转移的影响.
- 新型CGFS,HPCL和GHA模块有助于提高全球模型的稳定性和性能.
- 这种方法为构建更有弹性的联合学习系统提供了一个有希望的解决方案.
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