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:
Aug. 15, 2023

Filed:

Oct. 12, 2022
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Hugh Brendan McMahan, Seattle, WA (US);

Kunal Talwar, Sunnyvale, CA (US);

Li Zhang, Sunnyvale, CA (US);

Daniel Ramage, Seattle, WA (US);

Assignee:

GOOGLE LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 9/44 (2018.01); G06F 8/65 (2018.01); G06N 20/00 (2019.01); H04L 67/10 (2022.01); G06F 21/62 (2013.01); H04L 9/40 (2022.01); G06F 18/214 (2023.01); G06V 10/82 (2022.01); G06V 10/94 (2022.01);
U.S. Cl.
CPC ...
G06F 8/65 (2013.01); G06F 18/214 (2023.01); G06F 21/6245 (2013.01); G06N 20/00 (2019.01); H04L 63/00 (2013.01); H04L 67/10 (2013.01); G06V 10/82 (2022.01); G06V 10/95 (2022.01);
Abstract

Systems and methods for learning differentially private machine-learned models are provided. A computing system can include one or more server computing devices comprising one or more processors and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors cause the one or more server computing devices to perform operations. The operations can include selecting a subset of client computing devices from a pool of available client computing devices; providing a machine-learned model to the selected client computing devices; receiving, from each selected client computing device, a local update for the machine-learned model; determining a differentially private aggregate of the local updates; and determining an updated machine-learned model based at least in part on the data-weighted average of the local updates.


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