# Federated learning frameworks: quality and interoperability for biomedical research

## TL;DR

This paper reviews federated learning frameworks used in biomedical research, finding that while they are reusable and findable, they lack interoperability and privacy features needed for broader use.

## Contribution

The study systematically evaluates federated learning frameworks against FAIR principles and identifies key limitations in interoperability and privacy.

## Key findings

- Most frameworks perform well in findability and reusability but lack interoperability.
- Privacy-preserving techniques are rarely integrated into these frameworks.
- Horizontal architectures limit scalability in complex federated learning scenarios.

## Abstract

This review examines the current landscape of federated learning frameworks to evaluate their long-term sustainability, flexibility, and usability in biomedical research, where strict data regulations limit data sharing across institutions. Through a systematic literature analysis, the study assesses these frameworks against findability, accessibility, interoperability, and reusability for research software principles and compares reported use cases to framework functionalities to identify gaps in usability and scalability. The findings reveal that while most frameworks perform well in findability and reusability, they exhibit limited interoperability both among themselves and with specific software libraries. Although often developed for particular use cases, the technical foundations of these frameworks suggest potential for broader applicability. However, the scarce integration of privacy-preserving techniques and a predominant reliance on horizontal architectures may constrain their scalability in more complex federated learning scenarios. Ultimately, this analysis highlights the necessity for federated learning frameworks to evolve toward greater interoperability, flexibility, and privacy-awareness.

Graphical Abstract