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

• URL: http://arxiv.org/abs/2109.14046v1
• Date: Tue, 28 Sep 2021 21:01:30 GMT
• Title: 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
• Abstract summary: This paper develops two algorithms to achieve federated generalized linear mixed effect models (GLMM) The log-likelihood function of GLMM is approximated by two numerical methods, which supports federated decomposition of GLMM to bring to data. Experiment results demonstrate comparable (Laplace) and superior (Gaussian-Hermite) performances with simulated and real-world data.
• Score: 10.445522754737496
• License: http://creativecommons.org/licenses/by/4.0/
• 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.).

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