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Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources. (arXiv:2109.14046v2 [stat.ML] UPDATED)
stat.ML updates on arXiv.org arxiv.org
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 …
algorithms arxiv data distributed effects federated learning glmm learning mixed ml