Hyperparameter determination for a differentially private federated learning process
US11941520B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 9, 2020 |
| Grant date | Mar 26, 2024 |
| Priority date | — |
| Expiry date | Jul 3, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques regarding determining hyperparameters for a differentially private federated learning process are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a hyperparameter advisor component that determines a hyperparameter for a model of a differentially private federated learning process based on a defined numeric relationship between a privacy budget, a learning rate schedule, and a batch size.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.