Training a machine learning model in a distributed privacy-preserving environment
US11443226B2 · kind B2 · utility
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16Claims
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Key dates
| Filing date | May 17, 2017 |
| Grant date | Sep 13, 2022 |
| Priority date | — |
| Expiry date | Jan 27, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computer-implemented method applies labels to unlabeled public data for use by a global model. One or more processors train one or more local machine learning models with local private data to create one or more trained models. Processor(s) generate a label for each of the local private data using the one or more trained models, where each label describes the local private data, and then apply the label to unlabeled public data to create labeled public data. One or more processors then input the labeled public data into a global model that uses the public data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.