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Updated: May 29, 2025

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用NVIDIA FLARE进行联合学习的现实应用的概述
Holger R Roth1, Ziyue Xu1, Chester Chen1
1NVIDIA FLARE Team, NVIDIA Corporation, Santa Clara, CA, USA.
Journal of biopharmaceutical statistics
|February 3, 2025
概括
通过克服数据隐私法规,NVIDIA FLARE可以实现安全,协作式的医疗保健研究. 这种开源框架可以在不损害隐私的情况下为敏感的患者数据提供AI模型培训.
科学领域:
- 医疗保健人工智能的人工智能
- 合作研究合作研究.
- 数据 隐私 数据 隐私 数据
背景情况:
- 全球医疗保健挑战需要大规模的临床和科学合作.
- 对数据共享和患者隐私的监管约束阻碍了对代表性临床数据的访问.
- 现有的方法在协作研究中努力平衡数据实用性与隐私保护.
研究的目的:
- 介绍NVIDIA FLARE,一个开源的联合学习框架.
- 证明框架在解决临床问题和现实世界应用方面的能力.
- 展示NVIDIA FLARE如何实现协作研究,同时确保数据隐私和参与者控制.
主要方法:
- 开发一个开源的联合学习框架 (NVIDIA FLARE).
- 实施隐私保护技术,包括同态加密和差异隐私.
- 该框架应用于临床结果预测 (例如,COVID-19),联合统计和大型语言模型适应.
主要成果:
- 恩维迪亚FLARE成功地促进了对敏感患者数据的协作分析.
- 该框架能够预测临床结果,并支持其他现实世界的联合应用程序.
- 参与者在整个协作过程中保持数据治理.
结论:
- 在医疗保健领域,NVIDIA FLARE为保护隐私的协作研究提供了强大的解决方案.
- 该框架有效地解决了监管约束,同时提供了有价值的临床见解.
- 联合学习提供了一条可行的途径,通过安全的数据协作来推进医学研究.
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