生物医学联合学习的FeatureCloud平台:统一的方法
Julian Matschinske1, Julian Späth1, Mohammad Bakhtiari1
1University of Hamburg, Hamburg, Germany.
Journal of medical Internet research
|July 12, 2023
概括
功能云 (FeatureCloud) 是一个新的平台,使研究人员能够访问联合学习 (FL). 它简化了对各机构敏感数据的AI模型的训练,而不影响隐私.
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:
- 机器学习和人工智能的进步受到数据孤岛和隐私法规的阻碍.
- 联合学习 (FL) 允许在没有直接数据共享的情况下进行分布式模型培训.
- 目前的FL框架通常是专业化的,需要编程专业知识,并且缺乏中央平台.
研究的目的:
- 开发FeatureCloud,这是一个全集平台,用于联合学习.
- 为研究人员解决通用,用户友好的FL解决方案的缺口.
- 为算法开发人员和没有编程知识的用户提供FL.
主要方法:
- 功能云使用全球前端,全球后端和本地控制器架构.
- 使用Docker将本地组件集装箱化,确保与敏感数据的分离.
- 该平台使用五个数据集中的四个算法进行了评估,评估了准确性和运行时间.
主要成果:
- 功能云简化了多机构FL分析和算法实施的执行.
- 一个集成的AI存储允许轻松发布和重复使用联合算法.
- 该平台支持增强隐私的技术,并符合GDPR,产生与集中式方法相似的结果.
结论:
- 功能云集成了FL算法开发和执行,最大限度地减少了复杂性和基础设施障碍.
- 该平台显著提高了保护隐私的可访问性,在生物医学和其他领域进行分布式数据分析.
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