Unlearning with Fisher Masking

• URL: http://arxiv.org/abs/2310.05331v1
• Date: Mon, 9 Oct 2023 01:24:06 GMT
• Title: Unlearning with Fisher Masking
• Authors: Yufang Liu, Changzhi Sun, Yuanbin Wu, Aimin Zhou
• Abstract summary: Machine unlearning aims to revoke some training data after learning in response to requests from users, model developers, and administrators. Most previous methods are based on direct fine-tuning, which may neither remove data completely nor retain full performances on the remain data. We propose a new masking strategy tailored to unlearning based on Fisher information.
• Score: 20.763692349949245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine unlearning aims to revoke some training data after learning in response to requests from users, model developers, and administrators. Most previous methods are based on direct fine-tuning, which may neither remove data completely nor retain full performances on the remain data. In this work, we find that, by first masking some important parameters before fine-tuning, the performances of unlearning could be significantly improved. We propose a new masking strategy tailored to unlearning based on Fisher information. Experiments on various datasets and network structures show the effectiveness of the method: without any fine-tuning, the proposed Fisher masking could unlearn almost completely while maintaining most of the performance on the remain data. It also exhibits stronger stability compared to other unlearning baselines

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