通过精准信号引导，让模型更智能地删去特定数据。
Signal-Guided Optimization for Machine Unlearning
- 设计任务定制的细粒度引导信号，动态调节遗忘过程。
- 在14个基线中表现最优，兼顾遗忘效果与模型性能。
- 适合需要高精度隐私保护的场景，如医疗数据处理。
现有机器遗忘方法多依赖全局、粗粒度的干预策略，缺乏精确的引导信号，且无法在不同遗忘任务间提供可微分指导。由于训练过程中样本记忆强度差异，统一策略导致部分样本过度遗忘（损害模型效用），部分样本遗忘不足（残留信息易被隐私攻击利用）。本文提出GSUO框架，设计任务特异的细粒度引导信号，适用于随机子集和类别级遗忘任务。大量实验表明，GSUO在14个基线中均实现更优的遗忘效果与泛化能力，同时效率显著提升，验证了其在可靠机器遗忘中的有效性。
原文摘要 · Abstract (English)
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, which harms 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 and is applicable to both random-subset and class-wise forgetting tasks. Extensive experiments demonstrate that GSUO outperforms 14 baselines in terms of both unlearning effectiveness and generalization, while achieving high efficiency and significant speedups, validating its effectiveness for reliable machine unlearning.
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