Horizontal federated forest via secure aggregation

Abstract

One example method includes constructing a machine learning model which, when completed, is operable to screen candidates, from a group of candidates, to define a candidate pool that has specified characteristics. The constructing includes: broadcasting, from a central node to edges of a federation, an indication that construction of a random forest, of the machine learning model, has started; performing a federated feature categorization, by the central node based on information received from the edges, of a feature to be included in respective decision trees of the edges; based on the categorizing, broadcasting a feature category to the edges; performing, by the central node using respective purity information received from the edges, a federated purity calculation; and based on the federated purity calculation, broadcasting, by the central node to the edges, a winning feature split for the feature.