We explore the problem of selectively forgetting categories from trained CNN
classification models in the federated learning (FL). Given that the data used
for training cannot be accessed globally in FL, our insights probe deep into
the internal influence of each channel. Through the visualization of feature
maps activated by different channels, we observe that different channels have a
varying contribution to different categories in image classification. Inspired
by this, we propose a method for scrubbing the model clean of information about
particular categories. The method does not require retraining from scratch, nor
global access to the data used for training. Instead, we introduce the concept
of Term Frequency Inverse Document Frequency (TF-IDF) to quantize the class
discrimination of channels. Channels with high TF-IDF scores have more
discrimination on the target categories and thus need to be pruned to unlearn.
The channel pruning is followed by a fine-tuning process to recover the
performance of the pruned model. Evaluated on CIFAR10 dataset, our method
accelerates the speed of unlearning by 8.9x for the ResNet model, and 7.9x for
the VGG model under no degradation in accuracy, compared to retraining from
scratch. For CIFAR100 dataset, the speedups are 9.9x and 8.4x, respectively. We
envision this work as a complementary block for FL towards compliance with
legal and ethical criteria.

本文研究了在联邦学习中，通过实现 Term Frequency Inverse Document Frequency (TF-IDF) 对不同分类通道进行量化刻画，进而实现非对称遗忘（category forgetting）的目的，而不需要全局访问训练数据，仅通过不需要从头开始重新训练模型的渐进式剪枝来调整分类模型，实验结果表明相对于重新训练模型，无论是在 CIFAR10 数据集还是 CIFAR100 数据集中，本文的方法均可大幅加速非对称遗忘速度 8.9x ~ 9.9x，而且可以较准确地刻画通道与分类的关系，从而提供了一种合规性更高的选择。