US 2025/0094880 A1 · Fully Private Ensembles Using Knowledge Transfer

Abstract

Example embodiments of the present disclosure provide for an example method including obtaining a private dataset. The example method includes dividing the private dataset into a first data subset and a second data subset. The example method includes training a first teacher model using the first data subset and a second teacher model using the second data subset. The example method includes generating the aggregate teacher model based at least in part on the trained first teacher model and the trained second teacher model. The example method can include obtaining a modified dataset that was generated based on a private dataset and labeling the modified dataset by the aggregate teacher model. The example method can include training a publicly available student model using the labeled modified dataset.

Claims23

Dependent claims

A system, comprising:

one or more processors;
and
one or more computer-readable media storing instructions that are executable to cause the one or more processors to perform operations, the operations comprising:
obtaining a first private dataset;
dividing the first private dataset into at least a first data subset and a second data subset;
training a first teacher model using the first data subset;
training a second teacher model using the second data subset;
generating an aggregate teacher model based at least in part on the trained first teacher model and the trained second teacher model;
obtaining a modified dataset that was generated based on a private dataset;
labeling the modified dataset by the aggregate teacher model;
and
training a publicly available student model using the labeled modified dataset.

9 more elements

Dependencies are inferred from the claim text.

Fully Private Ensembles Using Knowledge Transfer

Life of this patent

Fully Private Ensembles Using Knowledge Transfer

Life of this patent

Abstract

Claims23