统计粗糙度引导的机器遗忘学习
Statistical Roughness-Informed Machine Unlearning
AI总结：
SRAGU通过统计粗糙度自适应梯度方法提升深度学习模型在大规模或对抗性删除下的稳定性与效用
摘要：
机器遗忘学习旨在从已训练模型中移除指定遗忘集的影响，同时保持对保留数据的效用。在现代深度网络中，大规模或对抗性删除下近似遗忘学习经常失效，这是因为层间异质性显著：一些层表现出稳定、良好的正则化表示，而其他层则脆弱、欠训练或过拟合，因此简单的更新分配可能触发灾难性遗忘或不稳定动态。我们提出了统计粗糙度自适应梯度遗忘学习（SRAGU），这是一种机制优先的遗忘学习算法，通过层间统计粗糙度操作化，利用重尾谱诊断对层权矩阵进行重尾谱诊断，重新分配遗忘更新。从适应性梯度遗忘学习（AGU）敏感信号计算得到的遗忘集开始，SRAGU为每一层估计一个权重观察者风格的重尾指数，将其映射到一个有界的谱稳定性权重，并使用此稳定性信号对AGU敏感度进行谱重加权，然后应用相同的mini-batch更新形式。这将遗忘运动集中在谱稳定的层中，同时抑制不稳定或过拟合层的更新，从而在困难删除下提高稳定性。我们通过行为对齐到黄金重新训练的参考模型进行遗忘评估，该参考模型从头开始在保留的数据上训练，使用经验预测分歧和KL到黄金代理在以遗忘为中心的查询集上进行评估；我们还报告了成员推断审计作为补充泄漏信号，将遗忘集点视为应被遗忘的成员进行评估。
英文摘要：
Machine unlearning aims to remove the influence of a designated forget set from a trained model while preserving utility on the retained data. In modern deep networks, approximate unlearning frequently fails under large or adversarial deletions due to pronounced layer-wise heterogeneity: some layers exhibit stable, well-regularized representations while others are brittle, undertrained, or overfit, so naive update allocation can trigger catastrophic forgetting or unstable dynamics. We propose Statistical-Roughness Adaptive Gradient Unlearning (SRAGU), a mechanism-first unlearning algorithm that reallocates unlearning updates using layer-wise statistical roughness operationalized via heavy-tailed spectral diagnostics of layer weight matrices. Starting from an Adaptive Gradient Unlearning (AGU) sensitivity signal computed on the forget set, SRAGU estimates a WeightWatcher-style heavy-tailed exponent for each layer, maps it to a bounded spectral stability weight, and uses this stability signal to spectrally reweight the AGU sensitivities before applying the same minibatch update form. This concentrates unlearning motion in spectrally stable layers while damping updates in unstable or overfit layers, improving stability under hard deletions. We evaluate unlearning via behavioral alignment to a gold retrained reference model trained from scratch on the retained data, using empirical prediction-divergence and KL-to-gold proxies on a forget-focused query set; we additionally report membership inference auditing as a complementary leakage signal, treating forget-set points as should-be-forgotten members during evaluation.
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