Systems and methods for privacy preserving training and inference of decentralized recommendation systems from decentralized data
US12088565B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 7, 2022 |
| Grant date | Sep 10, 2024 |
| Priority date | — |
| Expiry date | Feb 26, 2043 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L2209/46
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.