公开(公告)号：US12052315B2

公开(公告)日：2024-07-30

申请号：US17129579

申请日：2020-12-21

Applicant: Apple Inc.

Inventor： Stephen Cosman ,  Kalu Onuka Kalu ,  Marcelo Lotif Araujo ,  Michael Chatzidakis ,  Thi Hai Van Do ,  Alexis Hugo Louis Durocher ,  Guillaume Tartavel ,  Sowmya Gopalan ,  Vignesh Jagadeesh ,  Abhishek Bhowmick ,  John Duchi ,  Julien Freudiger ,  Gaurav Kapoor ,  Ryan M. Rogers

IPC: H04L67/1097 ,  G06F16/2457 ,  G06F16/438 ,  G06F16/44 ,  G06F18/214 ,  G06F21/62 ,  G06N3/063 ,  G06N20/00 ,  G06V10/774 ,  G06V10/82 ,  H04L67/00

CPC classification number: H04L67/1097 ,  G06F16/24578 ,  G06F16/438 ,  G06F16/447 ,  G06F18/2148 ,  G06F21/6254 ,  G06N3/063 ,  G06N20/00 ,  G06V10/7747 ,  G06V10/82 ,  H04L67/34

Abstract: Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to receive, at a client device, a machine learning model from a server, detect a usage pattern for a content item, store an association between the content item and the detected usage pattern in local data, train the machine learning model using local data for the content item with the detected usage pattern to generate a trained machine learning model, generate an update for the machine learning model, privatize the update for the machine learning model, and transmit the privatized update for the machine learning model to the server.

公开(公告)号：US20210166157A1

公开(公告)日：2021-06-03

申请号：US16501132

申请日：2020-01-17

Applicant: Apple Inc.

Inventor： Abhishek Bhowmick ,  John Duchi ,  Julien Freudiger ,  Gaurav Kapoor ,  Ryan M. Rogers

IPC: G06N20/20 ,  G06N5/04

Abstract: Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to perform operations comprising receiving a machine learning model from a server at a client device, training the machine learning model using local data at the client device, generating an update for the machine learning model, the update including a weight vector that represents a difference between the received machine learning model and the trained machine learning model, privatizing the update for the machine learning model, and transmitting the privatized update for the machine learning model to the server.

公开(公告)号：US20210192078A1

公开(公告)日：2021-06-24

申请号：US17129579

申请日：2020-12-21

Applicant: Apple Inc.

Inventor： Stephen Cosman ,  Kalu Onuka Kalu ,  Marcelo Lotif Araujo ,  Michael Chatzidakis ,  Thi Hai Van Do ,  Alexis Hugo Louis Durocher ,  Guillaume Tartavel ,  Sowmya Gopalan ,  Vignesh Jagadeesh ,  Abhishek Bhowmick ,  John Duchi ,  Julien Freudiger ,  Gaurav Kapoor ,  Ryan M. Rogers

IPC: G06F21/62 ,  G06K9/62 ,  H04L29/08 ,  G06N20/00 ,  G06N3/063

Abstract: Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to receive, at a client device, a machine learning model from a server, detect a usage pattern for a content item, store an association between the content item and the detected usage pattern in local data, train the machine learning model using local data for the content item with the detected usage pattern to generate a trained machine learning model, generate an update for the machine learning model, privatize the update for the machine learning model, and transmit the privatized update for the machine learning model to the server.

公开(公告)号：US11989634B2

公开(公告)日：2024-05-21

申请号：US16501132

申请日：2020-01-17

Applicant: Apple Inc.

Inventor： Abhishek Bhowmick ,  John Duchi ,  Julien Freudiger ,  Gaurav Kapoor ,  Ryan M. Rogers

IPC: G06F17/00 ,  G06N3/04 ,  G06N5/04 ,  G06N20/20

CPC classification number: G06N20/20 ,  G06N3/04 ,  G06N5/04

Abstract: Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processors to perform operations comprising receiving a machine learning model from a server at a client device, training the machine learning model using local data at the client device, generating an update for the machine learning model, the update including a weight vector that represents a difference between the received machine learning model and the trained machine learning model, privatizing the update for the machine learning model, and transmitting the privatized update for the machine learning model to the server.