Instance-Level Difficulty: A Missing Perspective in Machine Unlearning

Abstract:Current research on deep machine unlearning primarily focuses on improving or evaluating the overall effectiveness of unlearning methods while overlooking the varying difficulty of unlearning individual training samples. As a result, the broader feasibility of machine unlearning remains under-explored. This paper studies the cruxes that make machine unlearning difficult through a thorough instance-level unlearning performance analysis over various unlearning algorithms and datasets. In particular, we summarize four factors that make unlearning a data point difficult, and we empirically show that these factors are independent of a specific unlearning algorithm but only relevant to the target model and its training data. Given these findings, we argue that machine unlearning research should pay attention to the instance-level difficulty of unlearning.

Machine Learning (cs.LG)

arXiv:2410.03043 [cs.LG]

(or arXiv:2410.03043v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2410.03043 Focus to learn more arXiv-issued DOI via DataCite

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