Amazon Technologies, Inc.

University of California

Huili Chen

Tao Zhang

Farinaz Koushanfar

Bita Darvish Rouhani

Jie Ding

A system may create a localized machine learning model including one or more customized local parameter values using a global model and variance data. The localized machine learning model may be used by a device or cohort of devices to perform evaluations of data. The localized model may be trained based off a global model that is adjusted and then trained a certain number of steps, where the number of steps is based at least in part on the variance data. The variance data may include variance data from other device cohorts which is received from a remote device, which can also re-train the global model using the variance data and/or the localized machine learning model(s).

Updating machine learning models across devices

A method may include embedding, in a hidden layer and/or an output layer of a first machine learning model, a first digital watermark. The first digital watermark may correspond to input samples altering the low probabilistic regions of an activation map associated with the hidden layer of the first machine learning model. Alternatively, the first digital watermark may correspond to input samples rarely encountered by the first machine learning model. The first digital watermark may be embedded in the first machine learning model by at least training, based on training data including the input samples, the first machine learning model. A second machine learning model may be determined to be a duplicate of the first machine learning model based on a comparison of the first digital watermark embedded in the first machine learning model and a second digital watermark extracted from the second machine learning model.

Growing community of inventors

San Diego, CA, United States of America

Huili Chen