基础模型和联合学习:调查,分类学,挑战和实际见解
Cosmin-Andrei Hatfaludi1,2, Alex Serban1,2
1Foundational Technologies, Siemens SRL, Brasov, Romania.
PeerJ. Computer science
|September 24, 2025
概括
联合学习使私人数据基础模型的协作训练成为可能. 本调查对整合这些范式的方法进行了分类,并提供了实际指导方针,特别是在医疗保健应用中.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 联合学习 (FL) 允许在没有数据共享的情况下进行协作模式培训.
- 基础模型 (FMs) 的使用越来越多,需要扩大培训资源和私人数据集成.
- 整合FL和FM对于利用分布式私有数据至关重要.
研究的目的:
- 调查和分类整合联合学习和基础模型的技术方法.
- 提供现有方法的结构化概述和技术比较.
- 为实施提供实际指导方针,重点关注医疗保健领域.
主要方法:
- 4,200多篇文章的文献调查,缩小到250多篇评论论文.
- 基于模型开发生命周期的新型分类学的开发.
- 42种独特的集成方法的分类和技术比较.
主要成果:
- 识别和分类将联合学习与基础模型集成的方法.
- 基于复杂性,效率和可扩展性的方法的技术比较.
- 一个对最先进的技术和实际实施见解的结构化概述.
结论:
- 联合学习和基础模型提供了巨大的潜力,特别是在医疗保健等数据敏感领域.
- 开发的分类学为理解和选择集成方法提供了一个框架.
- 这项调查为研究人员和从业人员提供了全面的资源.
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