TY  - EJOU
AU  - Nehzati, Mohammadreza 

TI  - Phase 1 Implementation of a Federated Learning Network for Population-Scale Healthcare Data Harmonization: Operational Results from 47 U.S. Institutions
T2  - Journal of Intelligent Medicine and Healthcare

PY  - 2026
VL  - 4
IS  - 1
SN  - 2837-634X

AB  -  <b>Background:</b> Exponential growth of diverse clinical data presents challenges for real-time predictive analytics in healthcare. Federated learning offers a paradigm for multi-institutional model training without centralized data sharing, but large-scale deployment across diverse healthcare settings with real-world electronic health record (EHR) integration challenges remains limited. <b>Methods:</b> We implemented Phase 1 of a federated learning network deploying federated histogram-based XGBoost across 47 U.S. healthcare institutions from January to June 2023 as a quality improvement initiative. The system processes clinical data locally, transmitting only gradient and Hessian histograms with differential privacy (ε = 1.0, δ = 10<sup>−5</sup>). Primary outcomes were model discrimination (AUROC for 30-day readmission) and system reliability (uptime). Secondary outcomes included operational metrics (data harmonization efficiency, resource utilization). Exploratory clinical associations are reported with explicit causal warnings. <b>Results:</b> Phase 1 achieved AUROC = 0.76 (95% CI: 0.74–0.78) for 30-day readmission prediction, an 11.8% relative improvement over baseline (0.68). System uptime reached 99.1% with mean time to recovery of 2.3 min. Computational efficiency improved through algorithmic optimizations: 42.3% CPU reduction, 35.7% memory reduction, and 73.3% network bandwidth reduction. Data harmonization processing time decreased from 8.2 to 4.1 h per 100,000 records. Differential privacy provided formal protection (ε = 1.0, δ = 10<sup>−5</sup>) with membership inference attack AUC 0.523 (not significantly different from random guessing). Maximum subgroup AUROC disparity was reduced from 8.2% to 4.2% through bias mitigation. <b>Conclusions:</b> This first large-scale federated learning implementation across 47 diverse U.S. healthcare institutions demonstrates that conventional federated learning can be practically deployed, achieving meaningful predictive improvements and operational efficiency gains while maintaining strong privacy protections. However, substantial gaps between achieved (0.76) and aspirational (0.962) performance highlight challenges requiring future work. The study establishes an implementation blueprint for the field, provides transparency on real-world barriers, and identifies priority areas for research: reducing between-site heterogeneity (I<sup>2</sup> = 67%), improving automation rates (currently 78%), and addressing health equity gaps (6-point SES disparity). The infrastructure established provides a foundation for future phases, but clinical benefits require prospective randomized trials before causal claims can be made.
KW  - Federated learning; healthcare AI; XGBoost; differential privacy; readmission prediction; multi-site deployment; implementation science; health equity

DO  - 10.32604/jimh.2026.082983