Transfer learning without local data export in multi-node machine learning
US11164108B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 20, 2018 |
| Grant date | Nov 2, 2021 |
| Priority date | — |
| Expiry date | Sep 3, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/811
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A trained base model is distributed to a set of nodes. From a first node in the set of nodes, a first set of meta-metrics resulting from a transfer learning operation on the trained base model at the first node is collected. The transfer learning at the first node uses first local data available at the first node. The first node is clustered in a cluster with a second node from the set of nodes, in response to a meta-metric in the first set of meta-metrics being within a tolerance value of a corresponding meta-metric in a second set of meta-metrics collected from the second node. A normalized set of model parameters is constructed after an iteration of transfer learning or local learning at the first and second nodes. The normalized set of model parameters is distributed to the first node and the second node in the cluster.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.