通过联合学习彻底改变医疗数据分析:对应用程序,系统和未来方向的全面调查
Nisha Thorakkattu Madathil1, Fida K Dankar2, Marton Gergely1
1Department of Information Systems and Security, United Arab Emirates University, 15551 Al Ain, United Arab Emirates.
Computational and structural biotechnology journal
|July 3, 2025
概括
本调查分析了联合学习 (FL) 系统,将其与其他分布式方法进行比较. 它为设计高效,准确和私人医疗保健应用程序使用FL.提供了洞察力.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 分布式系统 分布式系统
背景情况:
- 联合学习 (FL) 能够在多个客户端进行协作模型培训,而无需数据集中.
- FL系统越来越多地被用于各种应用,包括医疗保健.
- 对现有FL系统的全面了解对于进一步开发至关重要.
研究的目的:
- 对现有的联合学习系统进行全面的调查和分析.
- 阐明FL系统的功能,并将其与替代分布式学习方法进行比较.
- 为设计高效,准确和保护隐私的医疗保健应用程序提供见解,使用FL.
主要方法:
- 2019年至2024年间发表的250多篇文章的系统审查和分析.
- FL系统组件的分类和检查,包括架构,规模,数据分区,框架,模型和聚合算法.
- 专注于医疗保健领域作为一个案例研究来定义构建块和识别挑战.
主要成果:
- 详细阐明FL系统的运行和与分布式学习替代方案的比较.
- 定义FL医疗保健系统的关键构建块:架构,联邦规模,数据分区,开源框架,ML模型和聚合算法.
- 识别和讨论设计和实施医疗保健FL系统的关键挑战.
结论:
- 联合学习为协作和保护隐私的机器学习提供了一个有前途的方法.
- 对FL系统的系统分析为开发强大的医疗保健应用提供了宝贵的见解.
- 概述了未来的研究方向,以应对当前的挑战,并在医疗保健中推进FL.
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