边界遗忘
Boundary Unlearning
摘要：
“被遗忘权”和有毒数据删除的实际需求需要高效的\textit{machine unlearning}技术，该技术使机器学习模型能够忘记学习，或者忘记一小部分训练数据及其谱系。最近关于深度神经网络（DNN）的机器遗忘的研究试图通过擦洗模型参数来破坏遗忘数据的影响。然而，它是昂贵的，由于大尺寸的参数空间。在本文中，我们将注意力从参数空间重新集中到DNN模型的决策空间，并提出了边界Unlearning，这是一种快速而有效的方法，可以从经过训练的DNN模型中删除整个类。其关键思想是移动原始DNN模型的决策边界，以模仿从头开始重新训练的模型的决策行为。我们提出了两种新的边界移动方法，即边界收缩和边界扩展，这两种方法都可以快速实现实用性和隐私性的保证。我们在CIFAR-10和Vggface 2数据集上对边界Unlearning进行了广泛的评估，结果表明，与从头开始重新训练相比，边界Unlearning可以有效地忘记图像分类和人脸识别任务中的遗忘类，预期速度分别为$17\times$和$19\times$。
英文摘要：
The practical needs of the ``right to be forgotten'' and poisoned data removal call for efficient \textit{machine unlearning} techniques, which enable machine learning models to unlearn, or to forget a fraction of training data and its lineage. Recent studies on machine unlearning for deep neural networks (DNNs) attempt to destroy the influence of the forgetting data by scrubbing the model parameters. However, it is prohibitively expensive due to the large dimension of the parameter space. In this paper, we refocus our attention from the parameter space to the decision space of the DNN model, and propose Boundary Unlearning, a rapid yet effective way to unlearn an entire class from a trained DNN model. The key idea is to shift the decision boundary of the original DNN model to imitate the decision behavior of the model retrained from scratch. We develop two novel boundary shift methods, namely Boundary Shrink and Boundary Expanding, both of which can rapidly achieve the utility and privacy guarantees. We extensively evaluate Boundary Unlearning on CIFAR-10 and Vggface2 datasets, and the results show that Boundary Unlearning can effectively forget the forgetting class on image classification and face recognition tasks, with an expected speed-up of $17\times$ and $19\times$, respectively, compared with retraining from the scratch.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
