Fig. 4: The estimated coefficients of Statsmodels (GLM), TF-Centralized (Tensorflow Probability) and TF-Fed-Patient (Tensorflow Probability with Federated Learning, using patient as the unit). | npj Digital Medicine

Fig. 4: The estimated coefficients of Statsmodels (GLM), TF-Centralized (Tensorflow Probability) and TF-Fed-Patient (Tensorflow Probability with Federated Learning, using patient as the unit).

From: Privacy-first health research with federated learning

Fig. 4

The plots show the coefficients and their 95 %confidence intervals of nine variables of different univariate logistic regression models. The significance of all models and variables is almost consistent with the original study: eight over nine variables have the same conclusions and only one (Acquisition status) does not (TF-Centralized and TF-Fed-Patient both show it is significant, while GLM and the original study state otherwise). In the original study, the variable has a p value of 0.06 which lies near the borderline of significance (p ≤ 0.05).

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