Core Concepts
Naive fine-tuning methods in machine unlearning struggle to forget targeted data because they retain information from pre-training, even when achieving optimal performance on the remaining dataset. This paper provides a theoretical explanation for this phenomenon and proposes a discriminative regularization technique to improve unlearning accuracy without sacrificing performance on the remaining data.
Abstract
Bibliographic Information:
Ding, M., Xu, J., & Ji, K. (2024). Why Fine-Tuning Struggles with Forgetting in Machine Unlearning? Theoretical Insights and a Remedial Approach. arXiv preprint arXiv:2410.03833v1.
Research Objective:
This paper investigates why fine-tuning (FT) methods, while effective in retaining model utility on remaining data, struggle to forget targeted data in machine unlearning. The authors aim to provide a theoretical understanding of this phenomenon and propose a remedial approach to improve unlearning accuracy.
Methodology:
The authors analyze FT methods within a linear regression framework, considering scenarios with both distinct and overlapping features between the forgetting and remaining datasets. They theoretically analyze the remaining and unlearning loss of FT models compared to models retrained from scratch (golden models). Based on their findings, they propose a discriminative regularization term to enhance unlearning in FT.
Key Findings:
- Naive FT methods fail to unlearn because the pretrained model retains information about the forgetting data, and fine-tuning does not effectively alter this retention.
- Removing the influence of forgetting data from the pretrained model significantly improves unlearning accuracy while preserving accuracy on the remaining data.
- Retaining overlapping features between remaining and forgetting datasets has minimal impact on unlearning accuracy, while discarding them decreases accuracy on the remaining data.
- The proposed discriminative regularization term, which encourages the model to learn incorrect labels for the targeted data, effectively reduces the unlearning loss gap between the fine-tuned model and the golden model.
Main Conclusions:
The theoretical analysis provides a clear explanation for the limitations of naive FT in machine unlearning. The proposed discriminative regularization method offers a practical and effective way to improve unlearning accuracy without significantly compromising performance on the remaining data.
Significance:
This research contributes to a deeper understanding of machine unlearning, particularly the challenges associated with forgetting in FT methods. The proposed regularization technique has practical implications for developing more effective and efficient machine unlearning algorithms.
Limitations and Future Research:
The theoretical analysis is conducted within a linear regression framework, and further investigation is needed to extend these findings to more complex models. Future research could explore the application of discriminative regularization to other unlearning techniques beyond FT.
Stats
Fine-tuning achieves 20.89% unlearning accuracy on CIFAR-10, compared to 100% for retraining from scratch.
The proposed KL-FT method achieves 99.17% unlearning accuracy on CIFAR-10, with a remaining accuracy of 99.06%.
On CIFAR-100, KL-FT achieves 95.20% unlearning accuracy and 99.26% remaining accuracy.
For SVHN, KL-FT achieves 97.24% unlearning accuracy and 99.95% remaining accuracy.
Quotes
"Fine-tuning, as one of the most widely used approaches in approximate unlearning, has demonstrated its empirical effectiveness. However, it can be observed in many studies [16, 30, 8, 17, 24] and our investigations in Table 1 that while fine-tuning may maintain the utility of the model on remaining data, it struggles to forget the targeted data."
"Our analysis shows that naive fine-tuning (FT) methods fail to unlearn the forgetting data because the pretrained model retains information about this data, and the fine-tuning process does not effectively alter that retention."
"Building on the aforementioned analysis, we introduce a discriminative regularization term to practically reduce the unlearning loss gap between the fine-tuned model and the golden model."