Unrolling SGD: Understanding Factors Influencing Machine Unlearning

Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, Nicolas Papernot

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Machine unlearning is the process through which a deployed machine learning model is made to forget about some of its training data points. While naively retraining the model from scratch is an option, it is almost always associated with large computational overheads for deep learning models. Thus, several approaches to approximately unlearn have been proposed along with corresponding metrics that formalize what it means for a model to forget about a data point. In this work, we first taxonomize approaches and metrics of approximate unlearning. As a result, we identify verification error, i.e., the \ell_2 difference between the weights of an approximately unlearned and a naively retrained model, as an approximate unlearning metric that should be optimized for as it subsumes a large class of other metrics. We theoretically analyze the canonical training algorithm, stochastic gradient descent (SGD), to surface the variables which are relevant to reducing the verification error of approximate unlearning for SGD. From this analysis, we first derive an easy-to-compute proxy for verification error (termed unlearning error). The analysis also informs the design of a new training objective penalty that limits the overall change in weights during SGD and as a result facilitates approximate unlearning with lower verification error. We validate our theoretical work through an empirical evaluation on learning with CIFAR-10, CIFAR-100, and IMDB sentiment analysis.

Original languageEnglish (US)
Title of host publicationProceedings - 7th IEEE European Symposium on Security and Privacy, Euro S and P 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages17
ISBN (Electronic)9781665416146
StatePublished - 2022
Externally publishedYes
Event7th IEEE European Symposium on Security and Privacy, Euro S and P 2022 - Genoa, Italy
Duration: Jun 6 2022Jun 10 2022

Publication series

NameProceedings - 7th IEEE European Symposium on Security and Privacy, Euro S and P 2022


Conference7th IEEE European Symposium on Security and Privacy, Euro S and P 2022

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality


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