A federated RuleFit method using differentially private histograms for consistent cutoffs, local GBDT rule generation, and federated dual averaging for l1-regularized coefficients matches centralized RuleFit performance in simulations and delivers interpretable results on real medical data.
Federated random forests can improve local performance of predictive models for various healthcare applications
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Federated Rule Ensemble Method in Medical Data
A federated RuleFit method using differentially private histograms for consistent cutoffs, local GBDT rule generation, and federated dual averaging for l1-regularized coefficients matches centralized RuleFit performance in simulations and delivers interpretable results on real medical data.