The Hybrid Homomorphic–Federated Learning Frameworks for Secure Population Health Prediction
Keywords:
Federated learning, homomorphic encryption, secure aggregation, differential privacy, population health, privacy-preserving machine learning.Abstract
Objective: Population health prediction requires learning from large, sensitive datasets scattered across hospitals, registries, and devices. This article proposes and details a hybrid privacy-preserving approach that marries Federated Learning (FL) with Homomorphic Encryption (HE) to enable multi-institutional modeling without exposing raw data or individual updates.
Methods: We synthesize advances in FL (e.g., FedAvg and secure aggregation), approximate-arithmetic HE (e.g., CKKS), and complementary safeguards (differential privacy, auditing) into a layered architecture for population-scale risk prediction (e.g., readmission, sepsis, multimorbidity, influenza/COVID-19 surges). We define trust and threat models, communication/computation pipelines, parameter choices, and evaluation protocols spanning utility, privacy, and systems performance.
Results: The proposed framework achieves end-to-end protection of data and model updates via secure aggregation and partially/fully homomorphic encryption for selected operations, while supporting realistic medical workflows. We outline algorithms for HE-friendly training and encrypted inference, discuss security against inference and poisoning attacks, and present a reproducible benchmarking plan.
Conclusions: Hybrid HE–FL can deliver clinically useful, generalizable population health models while reducing regulatory risk and cross-border data movement. We identify implementation patterns, performance trade-offs, and governance processes that convert cryptographic guarantees into deployable healthcare systems.
