Federated learning algorithms for generalized mixed-effects model (GLMM) on horizontally partitioned data from distributed sources
Loading...
Date
2022-10-16
Authors
Li, Wentao
Tong, Jiayi
Anjum, Md. M.
Mohammed, Noman
Chen, Yong
Jiang, Xiaoqian
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
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.).
Description
Keywords
Citation
BMC Medical Informatics and Decision Making. 2022 Oct 16;22(1):269