Recent data regulations necessitate machine unlearning (MU): The removal of
the effect of specific examples from the model. While exact unlearning is
possible by conducting a model retraining with the remaining data from scratch,
its computational cost has led to the development of approximate but efficient
unlearning schemes. Beyond data-centric MU solutions, we advance MU through a
novel model-based viewpoint: sparsification via weight pruning. Our results in
both theory and practice indicate that model sparsity can boost the
multi-criteria unlearning performance of an approximate unlearner, closing the
approximation gap, while continuing to be efficient. With this insight, we
develop two new sparsity-aware unlearning meta-schemes, termed `prune first,
then unlearn' and `sparsity-aware unlearning'. Extensive experiments show that
our findings and proposals consistently benefit MU in various scenarios,
including class-wise data scrubbing, random data scrubbing, and backdoor data
forgetting. One highlight is the 77% unlearning efficacy gain of fine-tuning
(one of the simplest approximate unlearning methods) in the proposed
sparsity-aware unlearning paradigm. Codes are available at
this https URL

该研究介绍了一种通过模型稀疏化实现机器遗忘的方法，通过剪枝模型的权重来提高近似遗忘的性能，开发了两种新的稀疏感知的遗忘元方案，“先剪枝再遗忘” 和 “稀疏感知遗忘”，在不同的情景下取得了一致的遗忘效果提高。