Apple Inc.

John Duchi

Gaurav Kapoor

Julien F Freudiger

Abhishek Bhowmick

Ryan M Rogers

Vignesh Jagadeesh

Sowmya Gopalan

Guillaume Tartavel

Kalu Onuka Kalu

Alexis Hugo Louis Durocher

Stephen Cosman

Marcelo Lotif Araujo

Michael Chatzidakis

Thi Hai Van Do

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.

User behavior model development with private federated learning

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.

Private federated learning with protection against reconstruction

Growing community of inventors

Menlo Park, CA, United States of America

John Duchi