ArticleBMC medical informatics and decision making2022
Federated learning algorithms for generalized mixed-effects model (GLMM) on horizontally partitioned data from distributed sources.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- D3MI: an efficient and powerful federated imputation method for bias reduction in the analysis of distributed incomplete data by accounting for within-site correlation and between-site heterogeneity.BMC medical informatics and decision making · 2026Article
- DASC-LOT Framework and S3 Metric: A Novel Evaluation and Benchmarking Method to Assess Initiation, Duration, and Spectrum of Antimicrobial Usage at Inpatient Hospital Settings.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026Article
- Federated Estimation of Population Pharmacokinetic Models with Exact Equivalence to Pooled-Data Analysis.Pharmaceutical research · 2026Article
- Unlocking multi-institutional insights into disease progression with PEAL as a lossless, one-shot federated learning solution.NPJ digital medicine · 2026Article
- Global approaches to infectious disease surveillance and modeling.Nature medicine · 2026Review
- dGAMLSS: an exact, distributed algorithm to fit Generalized Additive Models for Location, Scale, and Shape for privacy-preserving population reference charts.Bioinformatics (Oxford, England) · 2026Article
- Enabling the analysis of patient-level data across jurisdictions for research use: a real-world exploration of a federated likelihood approach.International journal of population data science · 2026Article
- Biomedical data repositories require governance for artificial intelligence/machine learning applications at every step.JAMIA open · 2025Article
- Unlocking efficiency in real-world collaborative studies: a multi-site international study with one-shot lossless GLMM algorithm.NPJ digital medicine · 2025Article
- D3MI: an efficient and powerful federated imputation method for bias reduction in the analysis of distributed incomplete data by accounting for within-site correlation and between-site heterogeneity.medRxiv : the preprint server for health sciences · 2025Article
- Technical and legal aspects of federated learning in bioinformatics: applications, challenges and opportunities.Frontiers in digital health · 2025Review
- Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics.JMIR medical informatics · 2024Article
- Federated systems for automated infection surveillance: a perspective.Antimicrobial resistance and infection control · 2024Review
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectivesThis paper developed federated solutions based on two approximation algorithms to achieve federated generalized linear mixed effect models (GLMM). The paper also proposed a solution for numerical errors and singularity issues. And showed the two proposed methods can perform well in revealing the significance of parameter in distributed datasets, comparing to a centralized GLMM algorithm from R package ('lme4') as the baseline model.
methodsThe log-likelihood function of GLMM is approximated by two numerical methods (Laplace approximation and Gaussian Hermite approximation, abbreviated as LA and GH), which supports federated decomposition of GLMM to bring computation to data. To solve the numerical errors and singularity issues, the loss-less estimation of log-sum-exponential trick and the adaptive regularization strategy was used to tackle the problems caused by federated settings.
resultsOur proposed method can handle GLMM to accommodate hierarchical data with multiple non-independent levels of observations in a federated setting. The experiment results demonstrate comparable (LA) and superior (GH) performances with simulated and real-world data.
conclusionWe modified and compared federated GLMMs with different approximations, which can support researchers in analyzing versatile biomedical data to accommodate mixed effects and address non-independence due to hierarchical structures (i.e., institutes, region, country, etc.).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.