通过Keycloak-Flower集成来增强对联机器学习的信任
Matthaeus Morhart1, Johanna Schwinn1, Seyedmostafa Sheikhalishahi1
1Digital Medicine, University Hospital of Augsburg, Augsburg, Germany.
Studies in health technology and informatics
|May 17, 2025
概括
医疗保健中的联合学习 (FL) 面临着采用挑战. 本研究将Keycloak与gRPC和Flower集成,以加强医疗组织中FL应用程序的安全性和访问管理.
科学领域:
- 计算机科学 计算机科学
- 医疗信息学 医疗信息学
- 网络安全 网络安全
背景情况:
- 联合学习 (FL) 在医疗保健方面表现有前途,但在现实世界中应用有限.
- 现有的FL框架,如Flower,需要加强对敏感医疗数据的安全性.
- 身份和访问管理对于在医疗保健组织中安全部署FL至关重要.
研究的目的:
- 改善医疗保健中联合学习的安全性和访问管理.
- 将强大的身份和访问管理解决方案Keycloak与Flower FL框架集成.
- 解决医疗保健组织内FL应用程序中的安全漏洞.
主要方法:
- 开发了一个轻量级的Python模块,将Keycloak与gRPC和Flower集成在一起.
- 在客户端代码执行之前,在服务器端实现代码验证.
- 在基本的原型环境中测试了集成系统.
主要成果:
- 成功地将Keycloak与gRPC和Flower集成,以加强身份和访问管理.
- 在FL框架内展示了客户端代码的服务器端验证机制.
- 建立了一个功能原型,验证了拟议的整合方法.
结论:
- 拟议的整合通过改进身份和访问管理来提高医疗保健中的FL安全性.
- 对于复杂的,现实世界的医疗保健部署,需要进一步的研究和全面的安全测试.
- 这种方法为医疗领域更安全,更广泛地采用联合学习提供了途径.
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