Unlocking multi-institutional insights into disease progression with PEAL as a lossless, one-shot federated learning solution.

Authors

Yicheng Shen, Ji Soo Kim, Chongliang Luo, Scott L Zeger, Robyn T Domsic, Ami A Shah, Jiayi Tong

Published in

NPJ digital medicine. Jun 18, 2026. Epub Jun 18, 2026.

Abstract

Multisite analysis of electronic health record (EHR) data presents unique opportunities for studying disease progression in real-world settings. However, privacy concerns, communication costs, and site-level heterogeneity pose significant challenges for analyzing longitudinal data. We introduce PEAL (Privacy-preserving Efficient Aggregation for Longitudinal data), a novel federated learning algorithm for fitting multi-level linear mixed-effects models with spline basis terms for nonlinear temporal trends. PEAL requires only a single-round transfer of summary statistics and produces results identical to using pooled individual participant data. Simulation studies demonstrate that PEAL accurately recovers fixed effects and variance components under realistic multi-level structures. We applied PEAL to real-world longitudinal datasets of systemic sclerosis patients from the Johns Hopkins and University of Pittsburgh Scleroderma Centers. This application shows our algorithm captures reasonable disease trajectories. Overall, PEAL provides a practical solution for distributed research networks studying rare diseases and time-evolving clinical outcomes by enabling lossless, communication-efficient, and privacy-preserving modeling.

PMID:
42315615
Bibliographic data and abstract were imported from PubMed on 19 Jun 2026.

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