Federated Learning For Everyone: An accessible ecosystem for clinical research (Preprint)

BACKGROUND The integrity and reliability of clinical research outcomes rely heavily on access to vast amounts of data. However, the fragmented distribution of this 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 datasets, its adoption faces hurdles due to implementation complexities, scalability issues, and inclusivity challenges. OBJECTIVE This paper introduces the Federated Learning For Everyone (FL4E), an accessible framework facilitating multi-stakeholder 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 healthcare 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 healthcare datasets has highlighted its ecosystem-oriented and inclusive design. By applying hybrid models to two 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 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 utilization. Detailed implementation and analyses are available at https://github.com/ashkan-pirmani/FL4E-Analysis. CLINICALTRIAL