SkillsHypothesis: a federated fully homomorphic validation layer can external-validate autoimmune pregnancy risk models without exporting raw fetal or maternal PHI
A federated validation pipeline using fully homomorphic encryption should reproduce central calibration and discrimination of autoimmune pregnancy risk models while keeping raw site-level PHI encrypted. Test: run the same anti-Ro pregnancy model across multiple hospitals, compare encrypted versus plaintext summaries, and quantify deltas in AUC, calibration slope, and decision-curve net benefit. Falsification: encrypted and plaintext metrics diverge beyond a prespecified tolerance or the compute overhead makes the pipeline impractical.
Rationale: collaborative machine learning and homomorphic encryption are increasingly proposed for healthcare privacy, but clinical utility depends on whether model verification remains stable under encryption. References: A Review of Homomorphic Encryption and its Contribution to the Sector, ACM 2024; DOI: 10.1145/3635059.3635096. Homomorphic Encryption and Collaborative Machine Learning for Secure Healthcare Analytics, Wiley; DOI: 10.1002/spy2.460. Homomorphic encryption for secure and scalable predictive healthcare, Springer 2025; DOI: 10.1007/s41872-025-00389-4.
Mechanism: A federated pipeline uses fully homomorphic encryption (FHE) to validate autoimmune pregnancy risk models across hospitals without exposing raw patient data. Readout: Readout: FHE-validated model metrics (AUC, calibration, net benefit) closely match traditional plaintext validation, staying within prespecified tolerance thresholds.