Hessian-Free Online Certified Unlearning

• URL: http://arxiv.org/abs/2404.01712v4
• Date: Thu, 06 Feb 2025 13:23:15 GMT
• Title: Hessian-Free Online Certified Unlearning
• Authors: Xinbao Qiao, Meng Zhang, Ming Tang, Ermin Wei,
• Abstract summary: We develop an online unlearning algorithm that achieves near-instantaneous data removal. We prove that our proposed method outperforms the state-of-the-art methods in terms of the unlearning and generalization guarantees.
• Score: 8.875278412741695
• License:
• Abstract: Machine unlearning strives to uphold the data owners' right to be forgotten by enabling models to selectively forget specific data. Recent advances suggest pre-computing and storing statistics extracted from second-order information and implementing unlearning through Newton-style updates. However, the Hessian matrix operations are extremely costly and previous works conduct unlearning for empirical risk minimizer with the convexity assumption, precluding their applicability to high-dimensional over-parameterized models and the nonconvergence condition. In this paper, we propose an efficient Hessian-free unlearning approach. The key idea is to maintain a statistical vector for each training data, computed through affine stochastic recursion of the difference between the retrained and learned models. We prove that our proposed method outperforms the state-of-the-art methods in terms of the unlearning and generalization guarantees, the deletion capacity, and the time/storage complexity, under the same regularity conditions. Through the strategy of recollecting statistics for removing data, we develop an online unlearning algorithm that achieves near-instantaneous data removal, as it requires only vector addition. Experiments demonstrate that our proposed scheme surpasses existing results by orders of magnitude in terms of time/storage costs with millisecond-level unlearning execution, while also enhancing test accuracy.

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