Systems and methods for noise agnostic federated learning
US12400430B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 16, 2022 |
| Grant date | Aug 26, 2025 |
| Priority date | — |
| Expiry date | Aug 21, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/82
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for noise agnostic federated learning are disclosed. A method may include a client computer program executed by an electronic device in a federated learning computer network comprising a plurality of clients: receiving, from a federated learning computer program, a data format having desirable noise characteristics; transforming a client data set comprising variable noise characteristics to the data format using a client generative adversarial network (GAN); generating client weights for the transformed client data set, wherein the client weights indicate features of the client data set; communicating the client weights to the federated learning computer program; receiving, from the federated learning computer program, adjusted weights, wherein the adjusted weights are based on the client weights and a plurality client weights received from the clients in the federated learning computer network; and updating the client weights for a client machine learning model using the adjusted weights.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.