FedPLC:联合学习与动态集群适应概念漂移在非IID数据的概念漂移
Qi Zhou1, Yantao Yu2,3, Jingxiao Ma1
1School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
通过分离数据异质性和概念漂移,FedPLC增强了物联网的联合学习 (FL). 这种新的框架提高了模型在动态,现实世界的环境中的性能和适应性.
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:
- 物联网 (IoT) 中的去中心化联合学习 (FL) 面临着来自非独立和相同分布 (Non-IID) 数据的挑战.
- 概念漂移通过引入时间不确定性,进一步降低了模型的融合和概括性.
- 现有的FL方法难以将数据异质性与动态转移区分开来,限制了适应性.
研究的目的:
- 提出FedPLC,一个新的FL框架,旨在解决物联网环境中的非IID数据和概念漂移.
- 为大规模物联网/边缘客户群增强模型的稳定性和细粒度的适应性.
主要方法:
- 介绍了原型定表示学习 (PARL),通过将样本嵌入与类原型对齐来稳定对抗噪音和转移的表示.
- 实施标签智能动态社区适应 (LDCA) 进行细粒度,标签级别的分类器头的重组,使得快速个性化和漂移意识的进化.
- 明确地将静态的非-IID异质性从时间概念漂移中解脱出来.
主要成果:
- 与最先进的方法相比,FedPLC在突然和增量概念漂移场景中表现出优越的性能.
- 关于时尚-MNIST,CIFAR-10和SVHN数据集的实验结果验证了该框架的有效性.
- 为大规模的物联网/边缘客户群实现强大而细粒度的适应.
结论:
- FedPLC有效地解决了非IID数据和概念漂移在物联网联合学习中的双重挑战.
- 拟议的PARL和LDCA机制为动态和异质边缘环境提供了强大的解决方案.
- 允许在现实世界中改进模型概括和融合,进化物联网部署.
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