Machine unlearning has garnered significant attention due to its ability to selectively erase knowledge obtained from specific training data samples in an already trained machine learning model. This capability enables data holders to adhere strictly to data protection regulations. However, existing unlearning techniques face practical constraints, often causing performance degradation, demanding brief fine-tuning post unlearning, and requiring significant storage. In response, this paper introduces a novel class of machine unlearning algorithms. First method is partial amnesiac unlearning, integration of layer-wise pruning with amnesiac unlearning. In this method, updates made to the model during training are pruned and stored, subsequently used to forget specific data from trained model. The second method assimilates layer-wise partial-updates into label-flipping and optimization-based unlearning to mitigate the adverse effects of data deletion on model efficacy. Through a detailed experimental evaluation, we showcase the effectiveness of proposed unlearning methods. Experimental results highlight that the partial amnesiac unlearning not only preserves model efficacy but also eliminates the necessity for brief post fine-tuning, unlike conventional amnesiac unlearning. Moreover, employing layer-wise partial updates in label-flipping and optimization-based unlearning techniques demonstrates superiority in preserving model efficacy compared to their naive counterparts.

翻译：机器遗忘因其能够从已训练好的机器学习模型中选择性地擦除特定训练数据样本的知识而备受关注。这一能力使得数据持有者能够严格遵守数据保护法规。然而，现有遗忘技术面临实际限制，常导致性能下降、需要在遗忘后进行短时微调以及大量存储需求。为此，本文提出了一类新型的机器遗忘算法。第一种方法是部分失忆遗忘，将分层剪枝与失忆遗忘相结合。在该方法中，训练过程中对模型的更新被剪枝并存储，随后用于从训练模型中遗忘特定数据。第二种方法将分层部分更新整合到标签翻转和基于优化的遗忘中，以减轻数据删除对模型性能的不利影响。通过详细的实验评估，我们展示了所提遗忘方法的有效性。实验结果表明，与传统失忆遗忘不同，部分失忆遗忘不仅能保持模型性能，还无需进行短时微调。此外，在标签翻转和基于优化的遗忘技术中采用分层部分更新，在保持模型性能方面优于其朴素版本。