Targeted Unlearning Using Perturbed Sign Gradient Methods With Applications On Medical Images

• URL: http://arxiv.org/abs/2505.21872v1
• Date: Wed, 28 May 2025 01:36:57 GMT
• Title: Targeted Unlearning Using Perturbed Sign Gradient Methods With Applications On Medical Images
• Authors: George R. Nahass, Zhu Wang, Homa Rashidisabet, Won Hwa Kim, Sasha Hubschman, Jeffrey C. Peterson, Ghasem Yazdanpanah, Chad A. Purnell, Pete Setabutr, Ann Q. Tran, Darvin Yi, Sathya N. Ravi,
• Abstract summary: We recast machine unlearning as a general-purpose tool for post-deployment model revision.<n>We propose a bilevel optimization formulation of boundary-based unlearning.<n>We provide convergence guarantees when first-order algorithms are used to unlearn.
• Score: 10.121286947766388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine unlearning aims to remove the influence of specific training samples from a trained model without full retraining. While prior work has largely focused on privacy-motivated settings, we recast unlearning as a general-purpose tool for post-deployment model revision. Specifically, we focus on utilizing unlearning in clinical contexts where data shifts, device deprecation, and policy changes are common. To this end, we propose a bilevel optimization formulation of boundary-based unlearning that can be solved using iterative algorithms. We provide convergence guarantees when first-order algorithms are used to unlearn. Our method introduces tunable loss design for controlling the forgetting-retention tradeoff and supports novel model composition strategies that merge the strengths of distinct unlearning runs. Across benchmark and real-world clinical imaging datasets, our approach outperforms baselines on both forgetting and retention metrics, including scenarios involving imaging devices and anatomical outliers. This work establishes machine unlearning as a modular, practical alternative to retraining for real-world model maintenance in clinical applications.

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