层攻击遗忘学习：通过层级别攻击和知识蒸馏实现快速且准确的机器遗忘
Layer Attack Unlearning: Fast and Accurate Machine Unlearning via Layer Level Attack and Knowledge Distillation
AI总结：
提出层攻击遗忘学习方法，结合Partial-PGD定位遗忘样本、仅用最后一层进行遗忘及知识蒸馏提升精度，实现快速准确的机器遗忘。
摘要：
近年来，机器学习算法在包含个人数据时，其训练数据集的隐私问题引起了严重关注。包括GDPR在内的各国法规赋予个人删除个人数据的权利，即“被遗忘权”或“删除权”。然而，关于如何在不损害整体机器学习性能的前提下，有效且实际地从训练集中删除所请求的个人数据的研究较少。在这项工作中，我们提出了一种在层级别上的快速且新颖的机器遗忘范式，称为层攻击遗忘学习，与现有机器遗忘算法相比，该方法高度准确且快速。我们引入了Partial-PGD算法来高效定位需要遗忘的样本。此外，受Forward-Forward算法启发，我们仅使用模型的最后一层进行遗忘过程。最后，我们使用知识蒸馏（KD）通过软标签信息可靠地从教师模型学习决策边界，以提高准确性性能。我们与最先进的机器遗忘模型进行了广泛实验，证明了我们的方法在准确性和端到端遗忘性能方面的有效性。
英文摘要：
Recently, serious concerns have been raised about the privacy issues related to training datasets in machine learning algorithms when including personal data. Various regulations in different countries, including the GDPR grant individuals to have personal data erased, known as 'the right to be forgotten' or 'the right to erasure'. However, there has been less research on effectively and practically deleting the requested personal data from the training set while not jeopardizing the overall machine learning performance. In this work, we propose a fast and novel machine unlearning paradigm at the layer level called layer attack unlearning, which is highly accurate and fast compared to existing machine unlearning algorithms. We introduce the Partial-PGD algorithm to locate the samples to forget efficiently. In addition, we only use the last layer of the model inspired by the Forward-Forward algorithm for unlearning process. Lastly, we use Knowledge Distillation (KD) to reliably learn the decision boundaries from the teacher using soft label information to improve accuracy performance. We conducted extensive experiments with SOTA machine unlearning models and demonstrated the effectiveness of our approach for accuracy and end-to-end unlearning performance.
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。
