贝叶斯联合推论用于估计基于非共享多中心数据集的统计模型
Marianne A Jonker1, Hassan Pazira1, Anthony Cc Coolen2,3
1Research Institute for Medical Innovation, Science Department IQ Health, Section Biostatistics, Radboud University Medical Center, Nijmegen, Netherlands.
Statistics in medicine
|April 8, 2024
概括
与联邦学习 (FL) 相比,贝叶斯联合推理 (BFI) 为分析多中心数据提供了一种更有效,更精确的方法. BFI通过从较小的数据集中获取更丰富的信息来改进FL,需要更少的计算周期.
科学领域:
- 计算统计的计算统计.
- 机器学习在医疗保健中的应用
- 多中心数据分析数据分析.
背景情况:
- 在多变量分析中,分析小数据集的预测因素是具有挑战性的.
- 多中心数据的结合受到监管和后勤问题的阻碍.
- 联合学习 (FL) 提供了一个解决方案,允许分析分布式数据而无需合并.
研究的目的:
- 改进和实施一个替代的贝叶斯联合推理 (BFI) 框架.
- 解决FL在多中心数据分析的效率和精度方面的局限性.
- 开发一种能够有效处理小数据集的方法.
主要方法:
- 为多中心数据开发了贝叶斯联合推理 (BFI) 框架.
- BFI从分布式数据集中推断出局部参数值和后部分布特征.
- 使用模拟和现实数据,比较BFI与FL的业绩.
主要成果:
- 通过推断后方参数分布特征,BFI捕获了超越 FL 的额外信息.
- 与FL的多个周期不同,BFI需要单个推断周期,以提高效率.
- 拟议的BFI方法证明了可量化的绩效改进.
结论:
- 贝叶斯联合推理 (BFI) 为联合学习 (FL) 提供了一个强大而有效的替代方案.
- 在多中心环境中分析小型分布式数据集时,BFI特别有利.
- 该框架增强了统计能力,而不影响数据隐私或要求数据聚合.
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