US 2025/0165863 A1Published application

Systems and methods for machine unlearning

OwnerJialei SHI, Konstantinos GOURGOULIAS +4inventors

InventorsJialei SHI, Konstantinos GOURGOULIAS, Sean MORAN, Najah GHALYAN, John BUFORD, Leandro RIOS

Life of this patent

Estimating the term…

Loading patent…

Abstract

A method may include: receiving a set of retain samples comprising retain features to retain in a pretrained machine learning model, and a set of forget samples comprising forget features to remove from the pretrained machine learning model; providing the set of retain samples to the pretrained machine learning model resulting in a retain output and the set of forget samples to the pretrained machine learning model, resulting in a forget output; generating a set of retain weights and a set of forget weights based on the retain output and the forget output; freezing the set of retain weights; setting each forget weight to an initial state; executing a training epoch using the pretrained machine learning model and the retain samples that retrains the forget weights using the retain samples; combining the retrained forget weights with the retained weights to form an unlearned machine learning model.

Claims20

Dependent claims

A method comprising:

receiving, by a computer program executed by a computer processor, a set of retain samples comprising a plurality of retain features to retain in a pretrained machine learning model, and a set of forget samples comprising a plurality of forget features to remove from the pretrained machine learning model;
providing, by the computer program, the set of retain samples to the pretrained machine learning model, wherein the pretrained machine learning model generates a retain output;
providing, by the computer program, the set of forget samples to the pretrained machine learning model, wherein the pretrained machine learning model generates a forget output;
generating, by the computer program and using an influence function, a set of retain weights and a set of forget weights based on the retain output and the forget output;
freezing, by the computer program, the set of retain weights;
setting, by the computer program, each forget weight to an initial state;
executing, by the computer program, a training epoch using the pretrained machine learning model and the retain samples, wherein the training epoch retrains the forget weights using the retain samples;
combining, by the computer program, the retrained forget weights with the retained weights to form an unlearned machine learning model;
and
deploying the unlearned machine learning model.

7 more elements

A non-transitory computer readable storage medium, including instructions stored thereon, which when read and executed by one or more computer processors, cause the one or more computer processors to perform steps comprising:

receiving a set of retain samples comprising a plurality of retain features to retain in a pretrained machine learning model, and a set of forget samples comprising a plurality of forget features to remove from the pretrained machine learning model;
providing the set of retain samples to the pretrained machine learning model, wherein the pretrained machine learning model generates a retain output;
providing the set of forget samples to the pretrained machine learning model, wherein the pretrained machine learning model generates a forget output;
generating, using an influence function, a set of retain weights and a set of forget weights based on the retain output and the forget output;
freezing the set of retain weights;
setting each forget weight to an initial state;
executing a training epoch using the pretrained machine learning model and the retain samples, wherein the training epoch retrains the forget weights using the retain samples;
combining the retrained forget weights with the retained weights to form an unlearned machine learning model;
and
deploying the unlearned machine learning model.

7 more elements

Dependencies are inferred from the claim text.