强大的两阶段联合学习,用于基于传感器的人类活动识别,标记噪音
Haifeng Sun1, Junping Yao1, Xiaojun Li2
1Rocket Force University of Engineering, Xi'an, 710025, People's Republic of China.
Scientific reports
|May 18, 2025
概括
人类活动识别的联合学习与杂的标签作斗争. 本研究介绍了LN-FHAR,这是一个强大的框架,通过解决数据质量和异质性挑战来提高模型性能.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 联合学习 (FL) 允许跨设备的协作模式培训.
- 佛罗里达州的人类活动识别 (HAR) 模型受到来自数据注释的标签噪声的阻碍.
- 现有的FL标签噪声方法在客户评估和数据聚合方面存在局限性.
研究的目的:
- 提出LN-FHAR,一个新的两阶段联合学习框架.
- 为了提高标签噪声的稳定性,并减轻HAR模型中的特征漂移.
- 解决数据异质性问题,改善噪音环境中的模型概括性.
主要方法:
- 客户端选择使用类级损失分析和高斯混合模型.
- 噪音强度训练与可靠的邻居样本过和原型规范化.
- 数据意识聚合按数据质量和数量加权客户贡献.
主要成果:
- LN-FHAR有效地减轻了标签噪声和数据异质性的合.
- 该框架在复杂的噪音环境中表现出强度和通用性.
- 在标签噪音下的联合人类活动识别模型中提高了性能.
结论:
- LN-FHAR提供了一个强大的解决方案,用于用标签噪声联合识别人类活动.
- 拟议的方法增强了客户质量评估和数据聚合策略.
- 这一框架促进了可靠的人工智能系统在现实世界,杂的数据场景中的发展.
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