Abstract
Current machine unlearning methods predominantly rely on global, coarse-grained intervention strategies. They lack precise pilot signals to guide the unlearning process and fail to provide differentiable guidance across different unlearning tasks. Due to the varying memorization strengths of samples during original training, such a uniform strategy leads to two problems: some samples are over-unlearned, harming model utility; while others are under-unlearned, leaving residual information that can be exploited by privacy attacks.
In this paper, we propose GSUO, a guidance-signal-aware unlearning optimization framework that designs task-specific fine-grained guidance signals to steer the unlearning process, applicable to both random-subset and class-wise forgetting tasks. Extensive experiments demonstrate that GSUO outperforms 14 baselines in unlearning effectiveness and generalization, while achieving high efficiency and significant speedups, validating its effectiveness for reliable machine unlearning.
Blogger's Review: GSUO offers a novel fine-grained signal-guided strategy that significantly enhances the effectiveness and efficiency of machine unlearning. In an era where privacy protection is increasingly critical, it presents a new solution for model security. The potential applications across various tasks warrant further exploration.