Federated learning algorithms for generalized mixed-effects model (GLMM) on horizontally partitioned data from distributed sources.
Objectives: This 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 revea...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 22; no. 1; pp. 1 - 13 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
10/16/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159721458&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159721458 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 10/16/2022 vid: 22 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 159721458 159721458 NLM36244993 159721458 10.1186/s12911-022-02014-1 NLM36244993 159721458 ppf: 1 ppct: 12 formats: tig: atl: Federated learning algorithms for generalized mixed-effects model (GLMM) on horizontally partitioned data from distributed sources. aug: au: Li, Wentao Tong, Jiayi Anjum, Md. Monowar Mohammed, Noman Chen, Yong Jiang, Xiaoqian affil: School of Biomedical Informatics, UTHealth, 7000 Fannin St, 77030, Houston, TX, USA sug: subj: Study Design Algorithms Probability Computer Simulation Linear Regression ab: Objectives: This 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.Methods: The 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.Results: Our 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.Conclusion: We 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.). pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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