Improved Localized Machine Unlearning Through the Lens of Memorization

Authors: Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Georgios Kaissis, Daniel Rueckert, Gintare Karolina Dziugaite, Eleni Triantafillou
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Machine Unlearning, Memorization, Localized Unlearning

Abstract:
Machine unlearning refers to removing the influence of a specified subset of training data from a machine learning model, efficiently, after it has already been trained. This is important for key applications, including making the model more accurate by removing outdated, mislabeled, or poisoned data. In this work, we study localized unlearning, where the unlearning algorithm operates on a (small) identified subset of parameters. Drawing inspiration from the memorization literature, we propose an improved localization strategy that yields strong results when paired with existing unlearning algorithms. We also propose a new unlearning algorithm, Deletion by Example Localization (DEL), that resets the parameters deemed-to-be most critical according to our localization strategy, and then finetunes them. Our extensive experiments on different datasets, forget sets and metrics reveal that DEL sets a new state-of-the-art for unlearning metrics, against both localized and full-parameter methods, while modifying a small subset of parameters, and outperforms the state-of-the-art localized unlearning in terms of test accuracy too.

Decision: reject
Reviewer scores: 5, 6, 6, 3

Reviews and discussion:
<thread>
creation date:
1730448860954

thread_type:
official_review

<thread_object>
creator: reviewer_eynl

<content>
summary:
This paper addresses the challenge of machine unlearning in a localized context by introducing a novel approach based on the concept of memorization. Following a comparison of existing methods, the authors identify data-dependent and gradient-dependent techniques as particularly effective. They refine the current criticality-based localization strategy, resulting in a new unlearning algorithm, “Deletion by Example Localization” (DEL). DEL enables localized unlearning by resetting and fine-tuning parameters identified as essential based on the calculated criticality of the parameters.

strengths:
1.	The paper is well-written, with a clear and concise logical flow. It begins by introducing localization as a preferred approach for model unlearning and then presents a cohesive and insightful perspective—that unlearning can be viewed as an extreme form of no memorization (lines 165-169)—which lends coherence and unity to their proposed method.
2.	The paper provides a comprehensive review of existing methods, thoroughly examining current approaches and establishing its own assumptions, such as the advantages of data-dependent over data-agnostic methods and the reasoning for utilizing gradient information. These insights serve as the foundation for their proposed method, DEL.
3.	The proposed method is both simple and effective, achieving state-of-the-art performance.

weaknesses:
1. This paper extensively discusses related work and motivations, primarily focusing on comparisons between existing methods. The proposed approach appears to be a straightforward combination of existing techniques, which may limit its novelty.
2. The results in Section 3 do not necessarily support the hypotheses in Section 5.1, as the observed improvements could be attributed to other factors. Thus, a more thorough theoretical explanation of the proposed method is needed.
3. This paper focuses exclusively on classification models, but I believe that “unlearning” in LLMs (i.e., model or knowledge editing) is a more pressing concern. It remains uncertain whether the conclusions drawn from vision classifiers in this paper can be directly applied to LLMs.
4. There are a few typos, although they don’t impact comprehension. For instance, in line 159, “$f(; \theta)$” might be intended as “$f(x; \theta)$.”

questions:
In Section 5.1, your paper presents several hypotheses. Could you provide a more detailed explanation of how your results support these hypotheses?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730620566452

thread_type:
official_review

<thread_object>
creator: reviewer_xsvs

<content>
summary:
The paper proposes the local unlearning algorithm Deletion by Example Localization, leveraging the memorization issue. The proposed algorithm first resets the parameters that are most critical based on the localization strategy and then finetunes them. The algorithm can be paired with various existing unlearning algorithms. The author validates experiments on different datasets with different metrics to show that the performance achieves state-of-the-art.

strengths:
1. The localized unlearning is new and meaningful research area and the motivation to leverage the memorization is reasonable and insightful.

2. The experiments and findings are validated with various metrics and existing unlearning algorithms and show consistently good results.

3. The paper is well formatted and arranged so that easy to understand.

weaknesses:
1. There are several mathematical definitions such as the Unlearning and Label memorization. However, I did not find close connections or logical relations between them. If necessary, I expect the author to use these definitions to derive some theorems closely based on the proposed algorithm. For example, it is difficult to see theoretically or empirically if the proposed algorithm can make distribution the same as the model trained without that data.

2. Following the above, I understand in this area, most justifications are more empirical. So, I think it's better to use some metrics that can support the definition (I.e., the same distribution as a retrained model).

questions:
1. I think the memorization property can vary from model scale. So, I am wondering if this memorization and proposed algorithm is available for most models since the evidence provided is empirical findings.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730696216531

thread_type:
official_review

<thread_object>
creator: reviewer_rshj

<content>
summary:
This paper introduced Deletion by Example Localization (DEL) method, which aimed at enhancing the machine unlearning by focusing on localized, a targeted data subset in neural networks. The traditional unlearning methods are removing the influence of certain data, making the model performance worse or requiring extensive retraining. However, DEL method used a selective approach by identifying a small subset of parameters that influenced by specific data points. This method can effectively remove the memory of specified data subset while persevering the model accuracy.

strengths:
This method achieves state-of-the-art unlearning performance while requiring a small modification on a subset of model parameters.

This method also minimized unnecessary parameter while preserving the model efficiency.

weaknesses:
The weakness of this method is limited experiments on the public dataset, only applied on CIFAR-10 and SVHN datasets, as well as the limitation on larger models.

questions:
Q1: From the Appendix A.4's algorithmn, the localization strategy is mainly from the magnitude of each weighted gradient for each mini-batch. Is the localization mask determined by each mini-batch? Is the localization mask fixed for different networks? If the mask is not accurate, does it affecting the accuracy? How sensitive is DEL to different choices of localization strategy.

Q2: Does the DEL method has any specific limitations when facing more complex or diverse data distributions?

Q3: Can DEL method adapted to other network architectures? What's the differences if it adapted to a customized network structure?

Q4: Does the performance different if using different hyper-parameters, such as learning rate, batch size, etc?

Q5:  In Table 7, the accuracy is getting better with higher percentage of parameters. Will the accuracy still getting better with 40%/50%?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730995358389

thread_type:
official_review

<thread_object>
creator: reviewer_j7jz

<content>
summary:
This work attempted to tackle the problem of localized unlearning by investigating it based on the memorization assumption and proposed DEL for some parameters with resetting and fine-tuning. The proposed method showed promising results on forgetting on a couple of benchmarks.

strengths:
- I liked the initial idea of investigating localized unlearning based on memorization. 
- The proposed method was partially successful on some forgetting benchmarks.

weaknesses:
- The method is based on a lot of assumptions without much justification, but with intuition. Thus, it is very hard to see if the proposed method is indeed ok in terms of unlearning (while preserving the rest!). 
- It is very hard to see the core contribution clearly due to poor writing. It was very hard to read and follow.
- Experiments look quite limited in terms of benchmarks (datasets, compared methods). I am afraid that the localized unlearning approach may hurt the preservation of remaining parts, but it is unclear if it is true.

questions:
- Please address the concerns in the weakness section.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732036023817

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General response to all reviewers

comment:
Dear reviewers,

We thank you for your time and valuable feedback! We have worked hard during the rebuttal, and we believe we have addressed all of the concerns comprehensively. We look forward to hearing back from the reviewers and discussing further.

We address each reviewer’s feedback individually in separate comments. In this common response, we will present new results that we ran during the rebuttal to address reviewers’ feedback.

A) We have implemented and compared against SSD of Foster et al. as an additional baseline, which we excluded from the original submission as it is contemporaneous work. Additionally, we include comparisons with the Influence Unlearning (IU) method proposed by Izzo et al. See the updated Table 2 and Table 8 in the revised paper. We find that DEL significantly outperforms these baselines too, on both types of forget sets and on all metrics considered.

B) We additionally conducted experiments on a new dataset, a subset of ImageNet (see the new Section A.6 and the results in Table 8 in the revised paper) using a ResNet-50 architecture. This dataset and architecture are significantly larger than the previous ones we considered, and the image resolution is significantly larger compared to our previous experiments. We find that, consistent with our findings on CIFAR-10 and SVHN, DEL outperforms prior methods on all unlearning metrics while also having better test accuracy compared to all prior localized unlearning approaches. 

Overall, we find that **DEL achieves SOTA results on three datasets / architectures** (CIFAR-10 with ResNet-18, SVHN with ViT, and ImageNet-100 with ResNet-50), **various forget sets** (IID and non-IID variations of CIFAR-10), **for two different unlearning metrics, against both localized and full-parameter unlearning methods. At the same time, DEL outperforms all previous localized unlearning methods in terms of utility metrics too, which indicates that our method preserves permissible knowledge** (see e.g. Table 2, Figure 4). In addition, **DEL is more robust to the parameter budget, outperforming the previous SOTA method SalUn across different budgets, and when paired with different unlearning algorithms** (Figure 3 and Figure 2).
</content>
</thread_object>

</thread>
<thread>
creation date:
1732459639225

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Looking forward to feedback on our rebuttal

comment:
Dear reviewers, 

We have worked very hard during the rebuttal and we believe we have addressed all of your concerns. We would really appreciate hearing back from you about our responses.
</content>
</thread_object>

</thread>
<thread>
creation date:
1733190829571

thread_type:
official_comment

<thread_object>
creator: reviewer_eynl

<content>
title:
My final rating is 5: marginally below the acceptance threshold

comment:
I understand the author's responses and efforts. I  change my rating into 5: marginally below the acceptance threshold.
</content>
</thread_object>

</thread>


Metareview:
metareview:
Summary. This paper introduced Deletion by Example Localization (DEL) method, which aimed at enhancing the machine unlearning by focusing on localized, a targeted data subset in neural networks. This method can effectively remove the memory of specified data subset while persevering the model accuracy.

Strengths. 
The idea of localized unlearning is interesting. 
The paper provides a detailed background to motivate the problem and a review of existing methods. 

Weaknesses. 
Parts of the unlearning process are unclear, which makes it difficult to understand the core contributions of the paper. While the paper clearly describes several localization and unlearning strategies, it is unclear what are the proposed strategies, are they novel, or are they a combination of known techniques? 
The experiments are presented on small datasets (CIFAR-10 with ResNet-18 and SVHN with ViT; ImageNet-100 with ResNet-50 added in rebuttal). These experiments seem limited in scope and impact compared to other unlearning papers. 
The paper focuses exclusively on the classification models, which is a limitation at this stage. 

Missing. 
A clear description of the algorithm with an emphasis on novel aspects will be useful. 
Experiments on models beyond classification can provide useful insights in the generalization of the proposed method. 

Reasons. 
The technical novelty of the proposed work is unclear. Localized unlearning methods exist, and this paper offers some modifications for the improvement. Experiments are mainly performed on classification models, which seems limited in scope and impact.