US 11,809,972 B2
Distributed machine learning for improved privacy
Vasyl Pihur, Santa Monica, CA (US); Subhash Sankuratripati, Playa Vista, CA (US); Dachuan Huang, Santa Monica, CA (US); Antonio Marcedone, New York, NY (US); Frederick Liu, Playa Vista, CA (US); and Ruogu Zeng, Los Angeles, CA (US)
Assigned to Snap Inc., Santa Monica, CA (US)
Filed by Snap Inc., Santa Monica, CA (US)
Filed on Apr. 21, 2022, as Appl. No. 17/726,338.
Application 17/726,338 is a continuation of application No. 16/158,010, filed on Oct. 11, 2018, granted, now 11,341,429.
Claims priority of provisional application 62/571,080, filed on Oct. 11, 2017.
Prior Publication US 2022/0245524 A1, Aug. 4, 2022
Int. Cl. G06N 20/00 (2019.01); G06N 5/04 (2023.01); H04L 51/18 (2022.01); H04L 51/52 (2022.01)
CPC G06N 20/00 (2019.01) [G06N 5/04 (2013.01); H04L 51/18 (2013.01); H04L 51/52 (2022.05)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
receiving, by a device from a server, an intermediate model;
accessing, by the device, training data based on a first message received from the server that indicates a list of active data identifiers, the training data comprising data that is private to the device;
storing a mapping between a plurality of models and corresponding lists of one or more input parameters used for training each of the plurality of models;
receiving, from the server, model data representing a state of the intermediate model;
training, by the device, the intermediate model based on the accessed training data and based on a determination that the device comprises a type of data associated with the intermediate model; and
transmitting, by the device, the trained intermediate model to the server without sharing the data that is private to the device.