Accessible Ecosystem for Clinical Research (Federated Learning for Everyone): Development and Usability Study

Area of Science:

• * Clinical research and data science.
• * Health informatics and collaborative platforms.

Background:

• * Clinical research relies on vast datasets, but data fragmentation and access barriers hinder progress.
• * Federated learning (FL) offers a solution for leveraging distributed data but faces implementation, scalability, and inclusivity challenges.

Purpose of the Study:

• * Introduce Federated Learning for Everyone (FL4E), an accessible framework for multistakeholder clinical research collaboration.
• * Simplify federated learning adoption through an ecosystem-based approach and customizable data decentralization.

Main Methods:

• * Introduce the

Main Results:

• * FL4E's ecosystem-oriented and inclusive design was validated using real-world healthcare data.
• * Hybrid models achieved performance comparable to fully federated models while reducing overhead.
• * The

Conclusions:

• * FL4E merges centralized and federated learning, offering an inclusive and customizable framework for clinical research.
• * The