Improved Localized Machine Unlearning Through the Lens of Memorization

Abstract

Machine unlearning refers to removing the influence of a specified subset of training data from a model efficiently, after it has already been trained. This is important for key applications, including making the model more accurate by removing outdated, mislabeled, or poisoned data. In this paper, we draw inspiration from prior work that attempts to identify where in the network a given example is memorized, to propose a new "localized unlearning" algorithm, Deletion by Example Localization (DEL). DEL has two components: a localization strategy that identifies critical parameters for a given set of examples, and a simple unlearning algorithm that finetunes only the critical parameters on the data we want to retain. Through extensive experiments, we find that our localization strategy outperforms prior strategies in terms of metrics of interest for unlearning and test accuracy, and pairs well with various unlearning algorithms. Our experiments on different datasets, forget sets, and metrics reveal that DEL outperforms prior work in producing better trade-offs between unlearning performance and accuracy.