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:
Sep. 27, 2022

Filed:

Oct. 27, 2021
Applicant:

Alipay (Hangzhou) Information Technology Co., Ltd., Zhejiang, CN;

Inventors:

Yilun Lin, Hangzhou, CN;

Hongjun Yin, Hangzhou, CN;

Jinming Cui, Hangzhou, CN;

Chaochao Chen, Hangzhou, CN;

Li Wang, Hangzhou, CN;

Jun Zhou, Hangzhou, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 21/62 (2013.01); G06K 9/62 (2022.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
G06F 21/6245 (2013.01); G06K 9/6256 (2013.01); G06N 3/0454 (2013.01); G06N 3/08 (2013.01);
Abstract

A computer-implemented method, medium, and system are disclosed. One example method includes determining multiple model bases by multiple service parties. A respective local service model is constructed by each service party. Respective local training samples are processed by each service party using the respective local service model to determine respective gradient data corresponding to each model basis. The respective gradient data is sent to a server. In response to determining that the first model basis satisfies a gradient update condition, corresponding gradient data of the first model basis received from each service party are combined to obtain global gradient data corresponding to the first model basis. The global gradient data is sent to each service party. Reference parameters in local model basis corresponding to the first model basis are updated by each service party using the global gradient data to train the respective local service model.


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