Federated training of machine learning models

Abstract

The invention provides a federated model based on locally trained machine learning models. In embodiments, a method includes: monitoring, by a computing device, cached data of an entity in a networked group of entities for changes in data, wherein the cached data includes model output data from worker models and a master feature model of the entity, and wherein the worker models and the master model comprise machine learning models; iteratively updating, by the computing device, parameter weights of the worker models and the master feature model based on the monitoring, thereby generating updated worker models and an updated master feature model; and providing, by the computing device, the updated worker models and an updated master feature model to a remote federated server for use in a federated model incorporating the updated worker models and an updated master feature model of the entity with other updated master feature models and other updated worker models of other entities in the networked group of entities.