Standardized API Design for Privacy-Preserving Federated Learning in FAIR-Compliant Biomedical Databases

The rapid expansion of machine learning methodologies in biomedical research has intensified the tension between the demand for large scale data analysis and the stringent privacy regulations governing sensitive health data. The integration of federated learning with FAIR-compliant databases necessitates a carefully engineered application programming interface (API) that reconciles multiple, partially competing requirements: preservation of the data governance, provenance, and access control mechanisms mandated by the FAIR principles; support for the iterative and stateful communication patterns inherent to federation learning protocols; maintenance of modularity to enable independent evolution and replacement of both database and machine learning components; and adherence to standards that promote long term interoperability and facilitate future extensions and ecosystem development. In this context, we propose a systematic methodology for designing and implementing standardised APIs that enable FAIR data repositories to support collaborative machine learning while respecting the governance, access control, and compliance requirements of the underlying database systems. This work contributes to a replicable framework that can be applied to other databases that seek to enable collaborative science at scale while maintaining the privacy protections essential for sensitive health information.