Privacy-enhanced training and deployment of machine learning models using client-side and server-side data

Abstract

Computer-implemented systems and methods for training a decentralized model for making a personalized recommendation. In one aspect, the method comprising: obtaining, using user activity data, client-side training data that includes features and training labels; and training, by the client device, a decentralized model in training rounds, wherein training, in each training round comprises: receiving, first data including a current server-side embedding generated by the server-side machine learning model, wherein the first data received from the server does not include any server-side data used in generating the current server-side embedding; generating, using the client-side machine learning model, a client-side embedding based on the client-side training data; updating, using the client-side embedding and the current server-side embedding and based on the training labels, the client-side machine learning model; generating, an updated client-side embedding; and transmitting second data including the updated client-side embedding for subsequent updating of the server-side machine learning model.