背景:临床研究结果的完整性和可靠性在很大程度上依赖于对大量数据的访问。然而,这些数据在多个机构中分散分布,以及道德和监管障碍,对访问相关数据提出了重大挑战。虽然联合学习提供了一个有前途的解决方案来利用来自零散数据集的见解,由于实施的复杂性,它的采用面临障碍,可扩展性问题,和包容性挑战。
目的:本文介绍了全民联合学习(FL4E),一个可访问的框架,促进临床研究中的多利益相关方合作。它侧重于通过基于生态系统的创新方法简化联合学习。
方法:“联邦程度”是FL4E的基本概念,允许灵活集成联合和集中学习模型。此功能通过使用户能够根据特定的医疗保健设置或项目需求选择数据分散的级别,提供了可自定义的解决方案,使联合学习更具适应性和效率。通过使用基于生态系统的协作学习策略,FL4E鼓励一个全面的平台来管理现实世界的数据,加强利益相关者之间的合作和知识共享。
结果:使用真实世界的医疗保健数据集评估FL4E的有效性突出了其面向生态系统和包容性的设计。通过将混合模型应用于现实环境中的两种不同的分析任务-分类和生存分析,我们已经有效地测量了不同背景下的“联邦程度”。这些评估表明,FL4E的混合模型不仅与完全联合模型的性能相匹配,而且避免了通常与这些模型相关的大量开销。实现这种平衡极大地增强了协作计划,并扩大了生态系统中分析可能性的范围。
结论:FL4E通过融合集中式和联合学习的好处,代表了协作临床研究的重要一步。其基于模块化生态系统的设计和“联邦程度”功能使其具有包容性,可定制的框架,适用于广泛的临床研究场景,承诺通过改进协作和数据使用来彻底改变该领域。相关的GitHub存储库中提供了详细的实施和分析。
BACKGROUND: The integrity and reliability of clinical research outcomes rely heavily on access to vast amounts of data. However, the fragmented distribution of these data across multiple institutions, along with ethical and regulatory barriers, presents significant challenges to accessing relevant data. While federated learning offers a promising solution to leverage insights from fragmented data sets, its adoption faces hurdles due to implementation complexities, scalability issues, and inclusivity challenges.
OBJECTIVE: This paper introduces Federated Learning for Everyone (FL4E), an accessible framework facilitating multistakeholder collaboration in clinical research. It focuses on simplifying federated learning through an innovative ecosystem-based approach.
METHODS: The \"degree of federation\" is a fundamental concept of FL4E, allowing for flexible integration of federated and centralized learning models. This feature provides a customizable solution by enabling users to choose the level of data decentralization based on specific health care settings or project needs, making federated learning more adaptable and efficient. By using an ecosystem-based collaborative learning strategy, FL4E encourages a comprehensive platform for managing real-world data, enhancing collaboration and knowledge sharing among its stakeholders.
RESULTS: Evaluating FL4E\'s effectiveness using real-world health care data sets has highlighted its ecosystem-oriented and inclusive design. By applying hybrid models to 2 distinct analytical tasks-classification and survival analysis-within real-world settings, we have effectively measured the \"degree of federation\" across various contexts. These evaluations show that FL4E\'s hybrid models not only match the performance of fully federated models but also avoid the substantial overhead usually linked with these models. Achieving this balance greatly enhances collaborative initiatives and broadens the scope of analytical possibilities within the ecosystem.
CONCLUSIONS: FL4E represents a significant step forward in collaborative clinical research by merging the benefits of centralized and federated learning. Its modular ecosystem-based design and the \"degree of federation\" feature make it an inclusive, customizable framework suitable for a wide array of clinical research scenarios, promising to revolutionize the field through improved collaboration and data use. Detailed implementation and analyses are available on the associated GitHub repository.