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Area of Science:

  • Biomedical research
  • Computer science
  • Data privacy

Background:

  • Strict data regulations in biomedical research hinder data sharing.
  • Federated learning (FL) offers a solution for collaborative analysis without centralizing sensitive data.
  • Existing FL frameworks require evaluation for suitability in this domain.

Purpose of the Study:

  • To assess the sustainability, flexibility, and usability of current federated learning frameworks for biomedical research.
  • To identify gaps in framework functionalities and scalability.
  • To evaluate frameworks against FAIR (Findability, Accessibility, Interoperability, Reusability) principles for research software.

Main Methods:

  • Systematic literature analysis of federated learning frameworks.
  • Assessment against FAIR principles for research software.
  • Comparison of reported use cases with framework functionalities.

Main Results:

  • Frameworks generally score well on findability and reusability.
  • Significant limitations exist in interoperability among frameworks and with other software libraries.
  • Limited integration of privacy-preserving techniques and a prevalence of horizontal architectures may hinder scalability.
  • Potential for broader applicability exists despite specialized development.

Conclusions:

  • Federated learning frameworks require enhancement in interoperability and flexibility for biomedical applications.
  • Increased adoption of privacy-preserving techniques is necessary for scalable and secure federated learning.
  • Future frameworks should prioritize modularity and broader compatibility to meet the demands of complex biomedical research.