Systems and methods for privacy preserving training and inference of decentralized recommendation systems from decentralized data

Abstract

A system and method are disclosed for training a recommendation system. The method includes initiating, at a server device, an item-vector matrix V, wherein the item-vector matrix V includes a value m related to a total number of items across one or more client devices and a value d representing a hidden dimension, transmitting the item-vector matrix V to each client device, wherein each client device trains a local matrix factorization model using a respective user vector U and the item-vector matrix V to generate a respective set of gradients on each respective client device, receiving, via a secure multi-party compute protocol, and from each client device, the respective set of gradients, updating the item-vector matrix V using the respective set of gradients from each client device to generate an updated item-vector matrix V and downloading the updated item-vector matrix V to at least one client device.