Federated learning algorithms for generalized mixed-effects model (GLMM) on horizontally partitioned data from distributed sources.

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

SUBMITTER: Li W 

PROVIDER: S-EPMC9569919 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature