针对医疗保健中高效联合学习的客户信息化招聘目标
Vincent Scheltjens1,2, Lyse Naomi Wamba Momo3, Wouter Verbeke4
1Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Kasteelpark Arenberg 10, Leuven, 3001, Belgium. vincent.scheltjens@kuleuven.be.
BMC medical informatics and decision making
|December 19, 2024
概括
本研究介绍了医疗保健中联合学习 (FL) 的新型客户招聘策略. 该方法增强了隐私,并通过根据数据分布和硬件选择代表性客户来减少培训时间,实现比传统方法更好或更好的性能.
科学领域:
- 医疗保健中的机器学习
- 医疗决策支持的人工智能
背景情况:
- 针对机器学习的医疗数据聚合带来了重大的隐私风险和行政负担.
- 联合学习 (FL) 允许在没有数据聚合的情况下进行分布式模型训练,但在客户选择和效率方面面临挑战.
- 现有的FL方法需要改进,以便在实际,保护隐私的医疗保健应用中实现.
研究的目的:
- 为医疗保健中的联合学习制定一个增强的客户招聘策略.
- 提高联合学习模型的效率和隐私保护能力.
- 通过结合本地硬件和数据特征来解决联合学习客户端选择方面的挑战.
主要方法:
- 通过整合本地硬件知识,扩展了之前的客户招聘方法.
- 根据代表性招募联合学习客户端,考虑当地目标分布差异,样本大小和硬件效率.
- 评估了医疗回归和分类任务的方法.
主要成果:
- 拟议的招聘方法实现了与中央和联邦方法相等或更高的绩效.
- 培训时间缩短了3-4倍,只需要一小部分的数据.
- 证明被排除在外的客户可以通过局部微调从联邦模式中受益.
结论:
- 通过使用本地目标分布,样本大小和硬件效率来定义一种新的客户招聘策略.
- 这种方法可以显著减少联合学习培训时间,而不会影响预测性能.
- 与标准的联合学习和中央方法相比,该方法增强了保护隐私的特性.
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