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arXiv · 2410.03043

Instance-Level Difficulty: A Missing Perspective in Machine Unlearning

Hammad Rizwan·Mahtab Sarvmaili·Hassan Sajjad·Ga Wu
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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.

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Hammad Rizwan, Mahtab Sarvmaili, Hassan Sajjad, Ga Wu. 2024-10-03. Instance-Level Difficulty: A Missing Perspective in Machine Unlearning. https://arxiv.org/abs/2410.03043

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