Similarity-Aware Machine Unlearning

Abstract:Machine unlearning removes the influence of user-specified training examples from a trained model, avoiding the need to retrain it from scratch. Localization-based methods improve unlearning efficiency by identifying a subset of influential model parameters. However, existing approaches select parameters based solely on forget-set importance, neglecting their role in retained dataset and often causing collateral damage to semantically similar retained examples. We address this limitation with a retain-aware localization method that considers parameter importance to both forgotten and retained data. We also introduce a retain-similar evaluation set, constructed using cosine similarity in the model embedding space, to directly measure collateral damage. Across eleven experimental settings on CIFAR-10 dataset and ResNet18 model, our method consistently reduces collateral damage while improving standard unlearning metrics, demonstrating the effectiveness of retain-aware localization for similarity-aware machine unlearning.

Machine Learning (cs.LG)

arXiv:2608.00246 [cs.LG]

(or arXiv:2608.00246v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.00246 Focus to learn more arXiv-issued DOI via DataCite

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