System and method of training heterogenous models using stacked ensembles on decentralized data

Abstract

Systems and methods are provided for machine learning in a distributed, privacy-preserving manner. Particularly, the decentralized system can share machine learning models in a protected manner by training a first sub-model with a first local data set at a first node and obfuscating the trained first sub-model as a first obfuscated sub-model. The model may be shared with a second node, that can construct a local instance of a stacked ensemble comprising the first obfuscated sub-model and a trainable parametric layer and train the local instance of the stacked ensemble with a second local data set accessible locally at the second node.