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[Submitted on 28 Sep 2021 (
v1
), last revised 7 Jun 2022 (this version, v2)]
Title:
Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources
View a PDF of the paper titled Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources, by Wentao Li and 5 other authors
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Abstract:
Objectives: This paper develops two algorithms to achieve federated generalized linear mixed effect models (GLMM), and compares the developed model's outcomes with each other, as well as that from the standard R package (`lme4').
Methods: The log-likelihood function of GLMM is approximated by two numerical methods (Laplace approximation and Gaussian Hermite approximation), which supports federated decomposition of GLMM to bring computation to data.
Results: Our developed method can handle GLMM to accommodate hierarchical data with multiple non-independent levels of observations in a federated setting. The experiment results demonstrate comparable (Laplace) and superior (Gaussian-Hermite) performances with simulated and real-world data.
Conclusion: We developed and compared federated GLMMs with different approximations, which can support researchers in analyzing biomedical data to accommodate mixed effects and address non-independence due to hierarchical structures (i.e., institutes, region, country, etc.).
Submission history
From: Wentao Li [
view email
]
[v1]
Tue, 28 Sep 2021 21:01:30 UTC (406 KB)
Tue, 7 Jun 2022 18:23:07 UTC (440 KB)
View a PDF of the paper titled Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources, by Wentao Li and 5 other authors
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