Distributed machine learning systems, apparatus, and methods
US11461690B2 · kind B2 · utility
Assignees
Inventors
Key dates
| Filing date | Jul 17, 2017 |
| Grant date | Oct 4, 2022 |
| Priority date | — |
| Expiry date | Mar 7, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F21/6245
- WIPO fieldMedical technology
- WIPO sectorInstruments
Abstract
A distributed, online machine learning system is presented. Contemplated systems include many private data servers, each having local private data. Researchers can request that relevant private data servers train implementations of machine learning algorithms on their local private data without requiring de-identification of the private data or without exposing the private data to unauthorized computing systems. The private data servers also generate synthetic or proxy data according to the data distributions of the actual data. The servers then use the proxy data to train proxy models. When the proxy models are sufficiently similar to the trained actual models, the proxy data, proxy model parameters, or other learned knowledge can be transmitted to one or more non-private computing devices. The learned knowledge from many private data servers can then be aggregated into one or more trained global models without exposing private data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.