Towards Aligned Data Forgetting via Twin Machine Unlearning

• URL: http://arxiv.org/abs/2501.08615v2
• Date: Thu, 23 Jan 2025 05:38:26 GMT
• Title: Towards Aligned Data Forgetting via Twin Machine Unlearning
• Authors: Zhenxing Niu, Haoxuan Ji, Yuyao Sun, Zheng Lin, Fei Gao, Yuhang Wang, Haichao Gao,
• Abstract summary: "Data forgetting" is often interpreted as achieving zero classification accuracy on such data. We propose a Twin Machine Unlearning (TMU) approach, where a twin unlearning problem is defined corresponding to the original unlearning problem. Our approach significantly enhances the alignment between the unlearned model and the gold model.
• Score: 19.84265071215051
• License:
• Abstract: Modern privacy regulations have spurred the evolution of machine unlearning, a technique enabling a trained model to efficiently forget specific training data. In prior unlearning methods, the concept of "data forgetting" is often interpreted and implemented as achieving zero classification accuracy on such data. Nevertheless, the authentic aim of machine unlearning is to achieve alignment between the unlearned model and the gold model, i.e., encouraging them to have identical classification accuracy. On the other hand, the gold model often exhibits non-zero classification accuracy due to its generalization ability. To achieve aligned data forgetting, we propose a Twin Machine Unlearning (TMU) approach, where a twin unlearning problem is defined corresponding to the original unlearning problem. Consequently, the generalization-label predictor trained on the twin problem can be transferred to the original problem, facilitating aligned data forgetting. Comprehensive empirical experiments illustrate that our approach significantly enhances the alignment between the unlearned model and the gold model.

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