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:
Feb. 08, 2022

Filed:

Jan. 18, 2019
Applicant:

Hypernet Labs, Inc., Palo Alto, CA (US);

Inventors:

Todd Allen Chapman, Palo Alto, CA (US);

Ivan James Ravlich, Los Altos, CA (US);

Christopher Taylor Hansen, Sunnyvale, CA (US);

Daniel Maren, Los Altos, CA (US);

Assignee:

HYPERNET LABS, INC., Redwood City, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 17/16 (2006.01); G06N 5/04 (2006.01); H04L 29/08 (2006.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 17/16 (2013.01); G06N 5/04 (2013.01); H04L 67/10 (2013.01);
Abstract

A distributed computing device generates a gradient descent matrix based on data received by the distributed computing device and a model stored on the distributed computing device. The distributed computing device calculates a sampled gradient descent matrix based on the gradient descent matrix and a random matrix. The distributed computing device iteratively executes a process to determine a consensus gradient descent matrix in conjunction with a plurality of additional distributed computing devices connected by a network to the distributed computing device. The consensus gradient descent matrix is based on the sampled gradient descent matrix and a plurality of additional sampled gradient decent matrices calculated by the plurality of additional distributed computing devices. The distributed computing device updates the model stored on the distributed computing device based on the consensus gradient descent matrix.


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