Asynchronously training machine learning models across client devices for adaptive intelligence
US11593634B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 19, 2018 |
| Grant date | Feb 28, 2023 |
| Priority date | — |
| Expiry date | Jan 24, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This disclosure relates to methods, non-transitory computer readable media, and systems that asynchronously train a machine learning model across client devices that implement local versions of the model while preserving client data privacy. To train the model across devices, in some embodiments, the disclosed systems send global parameters for a global machine learning model from a server device to client devices. A subset of the client devices uses local machine learning models corresponding to the global model and client training data to modify the global parameters. Based on those modifications, the subset of client devices sends modified parameter indicators to the server device for the server device to use in adjusting the global parameters. By utilizing the modified parameter indicators (and not client training data), in certain implementations, the disclosed systems accurately train a machine learning model without exposing training data from the client device.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.