为物联网支持的健康监测提供个性化的元联体学习
Zhenge Jia1, Tianren Zhou2, Zheyu Yan1
1Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, 46556 USA.
IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society
|August 8, 2025
概括
个性化Meta-Federated学习 (PMFed) 通过解决生物信号变异性来改善物联网健康监测. 该框架增强了模型个性化,以提高健康数据分析的准确性和效率.
科学领域:
- * 计算健康信息学和机器学习.
- * 物联网 (IoT) 用于远程健康监控.
- *生物信号的信号处理和模式识别.
背景情况:
- * 联合学习 (FL) 用于使用生物信号来保护隐私的健康监测.
- * 标准FL模型由于复杂的时间动态和生物信号的跨/内主体变异性,在不同受试者中表现不均.
- * 现有的方法难以平衡全球模型概括与个体受试者的需求.
研究的目的:
- * 引入个性化Meta-Federated学习 (PMFed) 框架,以实现个性化的物联网支持的健康监测.
- * 提高联合学习在分析特定学科生物信号方面的性能和效率.
- * 为了应对生物信号数据中主体间和主体内变异性的挑战.
主要方法:
- * 实施了基于域相似性的meta-federated学习范式,并采用了基于势头的新型模型聚合策略.
- * 开发了一种适应型模型个性化机制,以根据特定生物信号特征量身定制全球模型.
- * 通过使用支持物联网的计算系统对三个现实世界健康监测任务进行了框架评估.
主要成果:
- *PMFed表现出卓越的检测性能,F1和精度分别提高了9.4%和8.7%.
- * 与最先进的联合学习算法相比,实现了培训开支 (高达56.3%) 和吞吐量 (高达63.4%) 的显著降低.
- *通过调整全球模型以适应个体生物信号特征,展示了有效的个性化.
结论:
- *PMFed框架有效地解决了物联网健康监测中的生物信号变异性,优于现有的联合学习方法.
- *PMFed提供了个性化健康监测的强大解决方案,平衡隐私保护与个人特定模型适应.
- * 拟议的方法显著提高了检测精度,降低了计算成本,为高效的现实应用铺平了道路.
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