Efficient and Generalizable Second-Order Certified Unlearning: A Hessian-Free Online Model Updates Approach
Keywords: machine unlearning; certified data removal; privacy
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.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 830
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