Leveraging Distribution Matching to Make Approximate Machine Unlearning Faster

• URL: http://arxiv.org/abs/2507.09786v3
• Date: Mon, 04 Aug 2025 19:45:36 GMT
• Title: Leveraging Distribution Matching to Make Approximate Machine Unlearning Faster
• Authors: Junaid Iqbal Khan,
• Abstract summary: Approximate machine unlearning (AMU) enables models to forget' specific training data through specialized fine-tuning on a retained subset of training set.<n>We propose two complementary methods to accelerate arbitrary classification-oriented AMU method.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Approximate machine unlearning (AMU) enables models to `forget' specific training data through specialized fine-tuning on a retained (and forget) subset of training set. However, processing this large retained subset still dominates computational runtime, while reductions of unlearning epochs also remain a challenge. In this paper, we propose two complementary methods to accelerate arbitrary classification-oriented AMU method. First, \textbf{Blend}, a novel distribution-matching dataset condensation (DC), merges visually similar images with shared blend-weights to significantly reduce the retained set size. It operates with minimal pre-processing overhead and is orders of magnitude faster than state-of-the-art DC methods. Second, our loss-centric method, \textbf{Accelerated-AMU (A-AMU)}, augments the AMU objective to quicken convergence. A-AMU achieves this by combining a steepened primary loss to expedite forgetting with a differentiable regularizer that matches the loss distributions of forgotten and in-distribution unseen data. Our extensive experiments demonstrate that this dual approach of data and loss-centric optimization dramatically reduces end-to-end unlearning latency across both single and multi-round scenarios, all while preserving model utility and privacy. To our knowledge, this is the first work to systematically tackle unlearning efficiency by jointly designing a specialized dataset condensation technique with a dedicated accelerated loss function. Code is available at https://github.com/algebraicdianuj/DC_Unlearning.

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