GSUO: Machine Unlearning under Signal-Guided Optimization

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 difficulty of forgetting across different samples, this uniform strategy leads to two problems: when Boundary samples have not yet been forgotten, the entire model remains in an under-unlearned state, leaving residual information that can be exploited by privacy attacks; when normal samples have already been over-forgotten, the entire model falls into an over-unlearned state, thereby harming the model's utility. 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.