Similarity-Aware Machine Unlearning

• URL: http://arxiv.org/abs/2608.00246v1
• Date: Fri, 31 Jul 2026 19:41:55 GMT
• Title: Similarity-Aware Machine Unlearning
• Abstract summary: Machine unlearning removes the influence of user-specified training examples from a trained model, avoiding the need to retrain it from scratch.<n>Existing approaches select parameters based solely on forget-set importance, neglecting their role in retained dataset.<n>We address this limitation with a retain-aware localization method that considers parameter importance to both forgotten and retained data.
• Score: 0.49581497240446293
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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