Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an
Incompetent Teacher
- URL: http://arxiv.org/abs/2205.08096v2
- Date: Wed, 31 May 2023 11:53:38 GMT
- Title: Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an
Incompetent Teacher
- Authors: Vikram S Chundawat, Ayush K Tarun, Murari Mandal, Mohan Kankanhalli
- Abstract summary: We propose a novel machine unlearning method by exploring the utility of competent and incompetent teachers in a student-teacher framework to induce forgetfulness.
The knowledge from the competent and incompetent teachers is selectively transferred to the student to obtain a model that doesn't contain any information about the forget data.
We introduce the zero forgetting (ZRF) metric to evaluate any unlearning method.
- Score: 6.884272840652062
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Machine unlearning has become an important area of research due to an
increasing need for machine learning (ML) applications to comply with the
emerging data privacy regulations. It facilitates the provision for removal of
certain set or class of data from an already trained ML model without requiring
retraining from scratch. Recently, several efforts have been put in to make
unlearning to be effective and efficient. We propose a novel machine unlearning
method by exploring the utility of competent and incompetent teachers in a
student-teacher framework to induce forgetfulness. The knowledge from the
competent and incompetent teachers is selectively transferred to the student to
obtain a model that doesn't contain any information about the forget data. We
experimentally show that this method generalizes well, is fast and effective.
Furthermore, we introduce the zero retrain forgetting (ZRF) metric to evaluate
any unlearning method. Unlike the existing unlearning metrics, the ZRF score
does not depend on the availability of the expensive retrained model. This
makes it useful for analysis of the unlearned model after deployment as well.
We present results of experiments conducted for random subset forgetting and
class forgetting on various deep networks and across different application
domains.~Source code is at:
https://github.com/vikram2000b/bad-teaching-unlearning
Related papers
- Attribute-to-Delete: Machine Unlearning via Datamodel Matching [65.13151619119782]
Machine unlearning -- efficiently removing a small "forget set" training data on a pre-divertrained machine learning model -- has recently attracted interest.
Recent research shows that machine unlearning techniques do not hold up in such a challenging setting.
arXiv Detail & Related papers (2024-10-30T17:20:10Z) - MUSE: Machine Unlearning Six-Way Evaluation for Language Models [109.76505405962783]
Language models (LMs) are trained on vast amounts of text data, which may include private and copyrighted content.
We propose MUSE, a comprehensive machine unlearning evaluation benchmark.
We benchmark how effectively eight popular unlearning algorithms can unlearn Harry Potter books and news articles.
arXiv Detail & Related papers (2024-07-08T23:47:29Z) - TOFU: A Task of Fictitious Unlearning for LLMs [99.92305790945507]
Large language models trained on massive corpora of data from the web can reproduce sensitive or private data raising both legal and ethical concerns.
Unlearning, or tuning models to forget information present in their training data, provides us with a way to protect private data after training.
We present TOFU, a benchmark aimed at helping deepen our understanding of unlearning.
arXiv Detail & Related papers (2024-01-11T18:57:12Z) - Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning
Interference with Gradient Projection [56.292071534857946]
Recent data-privacy laws have sparked interest in machine unlearning.
Challenge is to discard information about the forget'' data without altering knowledge about remaining dataset.
We adopt a projected-gradient based learning method, named as Projected-Gradient Unlearning (PGU)
We provide empirically evidence to demonstrate that our unlearning method can produce models that behave similar to models retrained from scratch across various metrics even when the training dataset is no longer accessible.
arXiv Detail & Related papers (2023-12-07T07:17:24Z) - Federated Unlearning via Active Forgetting [24.060724751342047]
We propose a novel federated unlearning framework based on incremental learning.
Our framework differs from existing federated unlearning methods that rely on approximate retraining or data influence estimation.
arXiv Detail & Related papers (2023-07-07T03:07:26Z) - Deep Regression Unlearning [6.884272840652062]
We introduce deep regression unlearning methods that generalize well and are robust to privacy attacks.
We conduct regression unlearning experiments for computer vision, natural language processing and forecasting applications.
arXiv Detail & Related papers (2022-10-15T05:00:20Z) - Zero-Shot Machine Unlearning [6.884272840652062]
Modern privacy regulations grant citizens the right to be forgotten by products, services and companies.
No data related to the training process or training samples may be accessible for the unlearning purpose.
We propose two novel solutions for zero-shot machine unlearning based on (a) error minimizing-maximizing noise and (b) gated knowledge transfer.
arXiv Detail & Related papers (2022-01-14T19:16:09Z) - Fast Yet Effective Machine Unlearning [6.884272840652062]
We introduce a novel machine unlearning framework with error-maximizing noise generation and impair-repair based weight manipulation.
We show excellent unlearning while substantially retaining the overall model accuracy.
This work is an important step towards fast and easy implementation of unlearning in deep networks.
arXiv Detail & Related papers (2021-11-17T07:29:24Z) - Machine Unlearning of Features and Labels [72.81914952849334]
We propose first scenarios for unlearning and labels in machine learning models.
Our approach builds on the concept of influence functions and realizes unlearning through closed-form updates of model parameters.
arXiv Detail & Related papers (2021-08-26T04:42:24Z) - Certifiable Machine Unlearning for Linear Models [1.484852576248587]
Machine unlearning is the task of updating machine learning (ML) models after a subset of the training data they were trained on is deleted.
We present an experimental study of the three state-of-the-art approximate unlearning methods for linear models.
arXiv Detail & Related papers (2021-06-29T05:05:58Z) - Learning to Reweight with Deep Interactions [104.68509759134878]
We propose an improved data reweighting algorithm, in which the student model provides its internal states to the teacher model.
Experiments on image classification with clean/noisy labels and neural machine translation empirically demonstrate that our algorithm makes significant improvement over previous methods.
arXiv Detail & Related papers (2020-07-09T09:06:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.