Parameter sharing in federated learning
US11645582B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 27, 2020 |
| Grant date | May 9, 2023 |
| Priority date | — |
| Expiry date | Jul 16, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
One embodiment provides a method for federated learning across a plurality of data parties, comprising assigning each data party with a corresponding namespace in an object store, assigning a shared namespace in the object store, and triggering a round of federated learning by issuing a customized learning request to at least one data party. Each customized learning request issued to a data party triggers the data party to locally train a model based on training data owned by the data party and model parameters stored in the shared namespace, and upload a local model resulting from the local training to a corresponding namespace in the object store the data party is assigned with. The method further comprises retrieving, from the object store, local models uploaded to the object store during the round of federated learning, and aggregating the local models to obtain a shared model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.