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Fig. 7 | BMC Medical Informatics and Decision Making

Fig. 7

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

Fig. 7

The ROC curve with Area Under Curve (AUC) among centralized, Laplace, and Gauss–Hermite methods. The orange ROC curve is the centralized method without regularization and the Laplace approximation(i.e., R implementation in the ‘lme4’ package, which does not have an option for including regularization). AUC values are also included, a higher AUC value implicates better performance of the model. The green ROC curve is the 2-degree Gauss–Hermite method with regularization

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