When unlearning is free: leveraging low influence points to reduce computational costs

Explainable & Ethical AI

Published: arXiv: 2512.05254v1

Authors

Anat Kleiman Robert Fisher Ben Deaner Udi Wieder

Abstract

As concerns around data privacy in machine learning grow, the ability to unlearn, or remove, specific data points from trained models becomes increasingly important. While state of the art unlearning methods have emerged in response, they typically treat all points in the forget set equally. In this work, we challenge this approach by asking whether points that have a negligible impact on the model's learning need to be removed. Through a comparative analysis of influence functions across language and vision tasks, we identify subsets of training data with negligible impact on model outputs. Leveraging this insight, we propose an efficient unlearning framework that reduces the size of datasets before unlearning leading to significant computational savings (up to approximately 50 percent) on real world empirical examples.

Paper Summary

Problem

As machine learning becomes more prevalent, concerns around data privacy grow. This is because large datasets are often used to train models, but collecting and storing these datasets raises issues like data ownership disputes and evolving regulatory requirements. To address these challenges, researchers are looking for ways to remove specific data points from trained models, a process known as unlearning.

Key Innovation

The authors of this paper propose a new approach to unlearning that focuses on identifying and removing data points that have a negligible impact on the model's learning. They call these points "low influence points" and show that they can be safely removed without affecting the model's performance. This approach is unique because it challenges the traditional idea that all data points in the forget set are equal and need to be removed.

Practical Impact

The practical impact of this research is significant. By identifying and removing low influence points, models can be unlearned more efficiently, reducing computational costs and preserving data privacy. This is particularly important in real-world applications where unlearning requests are frequent and retraining a model from scratch can be expensive. The authors demonstrate that their approach can lead to significant computational savings (up to ∼50%) on real-world empirical examples.

Analogy / Intuitive Explanation

Imagine you're trying to learn a new language by studying a large textbook. However, as you flip through the pages, you notice that some sentences are written in a language you don't understand, and others are simply redundant. In this case, the "low influence points" are like the redundant sentences – they don't contribute much to your learning, and removing them won't affect your ability to learn the language. By identifying and removing these points, you can focus on the most important information and learn more efficiently. Similarly, the authors' approach helps models focus on the most important data points and learn more efficiently, while preserving data privacy.

Paper Information

Categories:

cs.LG

Published Date:

arXiv ID:

2512.05254v1

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