Federated Learning Algorithms for Generalized Mixed-Effects Model (GLMM) on Horizontally Partitioned Data From Distributed Sources

Authors

Wentao Li
Jiayi Tong
Md Monowar Anjum
Noman Mohammed
Yong Chen
Xiaoqian Jiang

Publication Date

10-16-2022

Journal

BMC Medical Informatics and Decision Making

Abstract

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.).

Keywords

Algorithms, Computer Simulation, Humans, Likelihood Functions, Linear Models, Research Design, GLMM, Federated learning, Mixed effects, Laplace approximation, Gauss–Hermite approximation

DOI

10.1186/s12911-022-02014-1

PMID

36244993

PMCID

PMC9569919

PubMedCentral® Posted Date

10-16-2022

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes