医疗保健的联合学习:减轻类不平衡和检测特征漂移
Jennifer Andres1,2, Hannes Hilberger1, Sten Hanke1
1Institute of eHealth, University of Applied Sciences - FH JOANNEUM, Graz, Austria.
Studies in health technology and informatics
|April 24, 2025
概括
医疗保健中的联合学习 (FL) 需要对数据质量进行强有力的监测. 这项研究开发了一个使用Flower的系统,提高模型公平性和检测数据漂移,这对于可靠的AI应用至关重要.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 医疗保健信息学 医疗保健信息学
背景情况:
- 联合学习 (FL) 能够对分散的医疗保健数据进行协作分析,但数据质量问题可能会损害模型的可靠性和公平性.
- 有效的监测系统对于在医疗保健机构成功实施FL至关重要.
- 挑战包括标签失衡和特征漂移,这可能会对模型性能产生负面影响.
研究的目的:
- 使用Flower框架开发和评估用于医疗保健数据的跨部门FL系统.
- 专注于监测指标和识别数据质量问题,如标签不平衡和特征漂移.
- 评估数据质量监测对FL模型性能和公平性的影响.
主要方法:
- 使用了花框架,用于跨的FL系统,并使用了LETHE项目的协调合成数据集.
- 在五个不同数据分布的客户上测试了该系统,其中一个客户有显著的标签不平衡.
- 使用 Prometheus 和 Grafana 实现实时度量跟踪 (精度,损失,MCC),以及集成的特征漂移检测 (Kolmogorov-Smirnov 测试).
主要成果:
- 联合模型显示出具有竞争力的性能,尽管基线模型最初表现优于它 (准确度:0.806比0.754).
- 一个定制的联邦平均化 (FedAvg) 算法,考虑到标签分布和数据集大小,显著提高了全球模型的马修斯相关系数 (MCC) 从0.111到0.349.
- 功能漂移检测成功集成,提供视觉警报.
结论:
- 监测系统对于确保医疗保健中FL模型的可靠性和公平性至关重要.
- 定制的FL聚合策略可以减轻数据异质性的影响,并提高模型性能.
- 需要进一步的研究来评估各种数据分布的定制FedAvg,并探索先进的隐私技术.
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