The rise of the phenomenon of the "right to be forgotten" has prompted research on machine unlearning, which grants data owners the right to actively withdraw data that has been used for model training, and requires the elimination of the contribution of that data to the model. A simple method to achieve this is to use the remaining data to retrain the model, but this is not acceptable for other data owners who continue to participate in training. Existing machine unlearning methods have been found to be ineffective in quickly removing knowledge from deep learning models. This paper proposes using a stochastic network as a teacher to expedite the mitigation of the influence caused by forgotten data on the model. We performed experiments on three datasets, and the findings demonstrate that our approach can efficiently mitigate the influence of target data on the model within a single epoch. This allows for one-time erasure and reconstruction of the model, and the reconstruction model achieves the same performance as the retrained model.

翻译：“被遗忘权”现象的兴起推动了机器遗忘研究，该权利赋予数据所有者主动撤回已用于模型训练数据的权利，并要求消除该数据对模型的影响。实现这一目标的简单方法是使用剩余数据重新训练模型，但这对于继续参与训练的其他数据所有者而言并不可接受。现有机器遗忘方法被发现无法快速消除深度学习模型中的知识。本文提出使用随机网络作为教师，以加速减轻被遗忘数据对模型的影响。我们在三个数据集上进行了实验，结果表明，我们的方法能够在单个周期内有效消除目标数据对模型的影响。这使得模型能够一次性擦除并重建，且重建后的模型性能与重新训练的模型相同。