arXiv:2605.21563v1 Announce Type: new Abstract: Recent reviews find that the vast majority of published healthcare federated learning (FL) studies never reach real-world deployment. We developed an embedding-based FL pipeline for iron deficiency prediction from routine full blood count (FBC) data and deployed it across real institutional environments at Amsterdam University Medical Centre (AUMC) and NHS Blood and Transplant (NHSBT), two clinical environments that differ markedly in iron deficiency prevalence, ferritin distribution, and subject populations. A frozen domain-specific haematology
The proliferation of federated learning research is now encountering deployment hurdles, making real-world implementation successes crucial for advancing the field beyond theoretical studies.
Successful deployment of federated learning in healthcare demonstrates a viable path for privacy-preserving AI, enabling collaboration across sensitive data silos and accelerating medical AI adoption.
This moves federated learning from a theoretical concept to a proven, practically deployable solution in healthcare, showing that real-world institutional complexities can be overcome.
- · Healthcare AI Developers
- · Hospitals and Healthcare Systems
- · Patients
- · Privacy-focused AI Solutions
- · Traditional Centralized Data Models
- · AI Models requiring extensive data sharing
Increased adoption of federated learning in healthcare due to validated deployment examples.
Accelerated development of AI tools addressing diverse medical conditions through collaborative, privacy-preserving data access.
New regulatory frameworks and industry standards emerging to support scalable federated AI deployments across highly regulated sectors.
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