Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources
This enables researchers to analyze biomedical data with hierarchical structures while preserving privacy, though it is incremental as it adapts existing approximations to a federated context.
The paper tackled the problem of fitting generalized linear mixed models (GLMM) on horizontally partitioned data in a federated learning setting, developing two algorithms based on Laplace and Gaussian-Hermite approximations that achieved comparable and superior performance to standard methods in experiments with simulated and real-world data.
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.).