用于模型遗忘的信号引导优化
Signal-Guided Optimization for Machine Unlearning
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中文总结 AI 辅助
针对当前模型遗忘方法缺乏精确引导信号的问题，提出GSUO框架，设计特定任务细粒度引导信号指导遗忘过程，适用于多种遗忘任务。实验表明，该框架在遗忘有效性、泛化、效率等方面表现出色，优于14个基线。
中文摘要 AI 辅助
当前的模型遗忘方法主要依赖全局、粗粒度的干预策略。它们缺乏精确的引导信号来指导遗忘过程，并且无法在不同的遗忘任务中提供可微的指导。由于原始训练期间样本的记忆强度不同，这种统一策略会导致两个问题：一些样本被过度遗忘，损害模型效用；而另一些样本则遗忘不足，留下可被隐私攻击利用的残留信息。在本文中，我们提出了GSUO，这是一个引导信号感知的遗忘优化框架，它设计特定于任务的细粒度引导信号来指导遗忘过程，适用于随机子集和按类遗忘任务。大量实验表明，GSUO在遗忘有效性和泛化方面优于14个基线，同时实现了高效率和显著的加速，验证了其对于可靠模型遗忘的有效性。
英文摘要
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.
