The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 05, 2023

Filed:

Sep. 08, 2020
Applicant:

Huawei Cloud Computing Technologies Co., Ltd., Gui Zhou Province, CN;

Inventors:

Lingyang Chu, Burnaby, CA;

Yutao Huang, Port Moody, CA;

Yong Zhang, Richmond, CA;

Lanjun Wang, Burnaby, CA;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06Q 30/02 (2023.01); G06Q 30/0201 (2023.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06Q 30/0201 (2013.01);
Abstract

A machine learning model is learned using secure vertical federated learning by receiving, by a network machine learning model, from a plurality of private machine learning models, a set of private machine learning model outputs. The set of private machine learning model outputs is based on data owned exclusively by each of the plurality of private machine learning models. The set of private machine learning model machine learning outputs is aligned based on sample IDs of the data. The network machine learning model, a prediction, the prediction being the output of the network model based on the set of private machine learning model outputs. Transmitting, by the network model, the prediction, to one of the plurality of private machine learning models, the one of the plurality of private machine learning models comprising labels. Receiving, by the network model, from the one of the plurality of private machine learning models, a loss based on the labels and the prediction, and calculating a gradient based on the loss, and updating a parameter of the network model based on the loss.


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