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FUNU: Boosting Machine Unlearning Efficiency by Filtering Unnecessary Unlearning

Zitong Li, Qingqing Ye, Haibo Hu

TL;DR

FUNU tackles the inefficiency of machine unlearning by filtering removal requests likely to be inconsequential to the retrained model. It builds a distance matrix from a pretraining feature space, uses a one-epoch reference model to derive a data-driven similarity threshold, and prunes $D_u$ to form $D_u^+$ so that unlearning on $D_u^+$ approximates retraining within a provable bound $\epsilon$. The approach yields theoretical privacy guarantees and demonstrates substantial time savings and robust model privacy across MNIST, CIFAR-10, CIFAR-100, including effective integration with SISA, while adapting to random and class removal scenarios. Overall, FUNU provides a practical, parameter-tunable, and adaptable framework to accelerate unlearning without compromising the right to be forgotten.

Abstract

Machine unlearning is an emerging field that selectively removes specific data samples from a trained model. This capability is crucial for addressing privacy concerns, complying with data protection regulations, and correcting errors or biases introduced by certain data. Unlike traditional machine learning, where models are typically static once trained, machine unlearning facilitates dynamic updates that enable the model to ``forget'' information without requiring complete retraining from scratch. There are various machine unlearning methods, some of which are more time-efficient when data removal requests are fewer. To decrease the execution time of such machine unlearning methods, we aim to reduce the size of data removal requests based on the fundamental assumption that the removal of certain data would not result in a distinguishable retrained model. We first propose the concept of unnecessary unlearning, which indicates that the model would not alter noticeably after removing some data points. Subsequently, we review existing solutions that can be used to solve our problem. We highlight their limitations in adaptability to different unlearning scenarios and their reliance on manually selected parameters. We consequently put forward FUNU, a method to identify data points that lead to unnecessary unlearning. FUNU circumvents the limitations of existing solutions. The idea is to discover data points within the removal requests that have similar neighbors in the remaining dataset. We utilize a reference model to set parameters for finding neighbors, inspired from the area of model memorization. We provide a theoretical analysis of the privacy guarantee offered by FUNU and conduct extensive experiments to validate its efficacy.

FUNU: Boosting Machine Unlearning Efficiency by Filtering Unnecessary Unlearning

TL;DR

FUNU tackles the inefficiency of machine unlearning by filtering removal requests likely to be inconsequential to the retrained model. It builds a distance matrix from a pretraining feature space, uses a one-epoch reference model to derive a data-driven similarity threshold, and prunes to form so that unlearning on approximates retraining within a provable bound . The approach yields theoretical privacy guarantees and demonstrates substantial time savings and robust model privacy across MNIST, CIFAR-10, CIFAR-100, including effective integration with SISA, while adapting to random and class removal scenarios. Overall, FUNU provides a practical, parameter-tunable, and adaptable framework to accelerate unlearning without compromising the right to be forgotten.

Abstract

Machine unlearning is an emerging field that selectively removes specific data samples from a trained model. This capability is crucial for addressing privacy concerns, complying with data protection regulations, and correcting errors or biases introduced by certain data. Unlike traditional machine learning, where models are typically static once trained, machine unlearning facilitates dynamic updates that enable the model to ``forget'' information without requiring complete retraining from scratch. There are various machine unlearning methods, some of which are more time-efficient when data removal requests are fewer. To decrease the execution time of such machine unlearning methods, we aim to reduce the size of data removal requests based on the fundamental assumption that the removal of certain data would not result in a distinguishable retrained model. We first propose the concept of unnecessary unlearning, which indicates that the model would not alter noticeably after removing some data points. Subsequently, we review existing solutions that can be used to solve our problem. We highlight their limitations in adaptability to different unlearning scenarios and their reliance on manually selected parameters. We consequently put forward FUNU, a method to identify data points that lead to unnecessary unlearning. FUNU circumvents the limitations of existing solutions. The idea is to discover data points within the removal requests that have similar neighbors in the remaining dataset. We utilize a reference model to set parameters for finding neighbors, inspired from the area of model memorization. We provide a theoretical analysis of the privacy guarantee offered by FUNU and conduct extensive experiments to validate its efficacy.

Paper Structure

This paper contains 25 sections, 2 theorems, 5 equations, 6 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Problem setting and existing solutions
  3. Problem setting
  4. Solutions by data prototype discovery
  5. Adaptation to unnecessary unlearning problem
  6. Limitations of existing solutions
  7. FUNU: an unnecessary unlearning filtering method
  8. Calculate distance matrix
  9. Establish similarity condition
  10. Filter removal requests
  11. Privacy guarantee
  12. Experiment
  13. General experiment setting
  14. Evaluation on FUNU
  15. Experiment setting.
  16. ...and 10 more sections

Key Result

theorem 1

Suppose that for model $M_r$ and $M_u$, the logarithms of final FC layer output, denoted as $log(M_r)$ and $log(M_u)$, are $\lambda_1$-Lipschitz and $\lambda_2$-Lipschitz, and that $\| log(M_r(x)) - log(M_u(x)) \| \leq \delta$ on $D_r$, then where $p_u$ is the output distribution of $M_u$, and $p_r$ is that of $M_r$. $n$ is the size of $D_u^+$.

Figures (6)

  • Figure 1: Example of unnecessary unlearning
  • Figure 2: Existing solutions and our method in class removal scenario. As for existing solutions, they tend to select samples with many neighbors in the entire dataset as $D_u^+$. As the proportion of such samples is fixed throughout the dataset, thus the proportion of $D_u^+$ would be consistent with that and be high. However, in our design, as we compare the removal requests with the remaining dataset, thus the proportion of $D_u^+$ would be low.
  • Figure 3: The procedure of FUNU
  • Figure 4: Timing of FUNU and existing solutions
  • Figure 5: $P^-$ of different methods
  • ...and 1 more figures

Theorems & Definitions (4)

  • Example 1.1
  • definition 1: Unnecessary Unlearning
  • theorem 1
  • theorem 2