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Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions

Sadia Qureshi, Thanveer Shaik, Xiaohui Tao, Haoran Xie, Lin Li, Jianming Yong, Xiaohua Jia

TL;DR

Incremental Unlearning (IU) addresses privacy regulations by enabling targeted removal of data influence from ML models without full retraining. The paper surveys a taxonomy of IU techniques—Reinforcement Unlearning, Continual Learning via Selective Forgetting, Corrective Unlearning, and Federated Unlearning—and details methods, verification, metrics, and datasets. It highlights key challenges (adaptive training, memory, catastrophic forgetting, and security risks) and proposes practical solutions (fine-tuning, approximation, distributed unlearning) along with future directions across NLP, imaging, and recommender systems. The work underscores the importance of scalable, verifiable, and bias-aware unlearning to realize privacy-preserving, adaptive AI in real-world settings.

Abstract

The growing demand for data privacy in Machine Learning (ML) applications has seen Machine Unlearning (MU) emerge as a critical area of research. As the `right to be forgotten' becomes regulated globally, it is increasingly important to develop mechanisms that delete user data from AI systems while maintaining performance and scalability of these systems. Incremental Unlearning (IU) is a promising MU solution to address the challenges of efficiently removing specific data from ML models without the need for expensive and time-consuming full retraining. This paper presents the various techniques and approaches to IU. It explores the challenges faced in designing and implementing IU mechanisms. Datasets and metrics for evaluating the performance of unlearning techniques are discussed as well. Finally, potential solutions to the IU challenges alongside future research directions are offered. This survey provides valuable insights for researchers and practitioners seeking to understand the current landscape of IU and its potential for enhancing privacy-preserving intelligent systems.

Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions

TL;DR

Incremental Unlearning (IU) addresses privacy regulations by enabling targeted removal of data influence from ML models without full retraining. The paper surveys a taxonomy of IU techniques—Reinforcement Unlearning, Continual Learning via Selective Forgetting, Corrective Unlearning, and Federated Unlearning—and details methods, verification, metrics, and datasets. It highlights key challenges (adaptive training, memory, catastrophic forgetting, and security risks) and proposes practical solutions (fine-tuning, approximation, distributed unlearning) along with future directions across NLP, imaging, and recommender systems. The work underscores the importance of scalable, verifiable, and bias-aware unlearning to realize privacy-preserving, adaptive AI in real-world settings.

Abstract

The growing demand for data privacy in Machine Learning (ML) applications has seen Machine Unlearning (MU) emerge as a critical area of research. As the `right to be forgotten' becomes regulated globally, it is increasingly important to develop mechanisms that delete user data from AI systems while maintaining performance and scalability of these systems. Incremental Unlearning (IU) is a promising MU solution to address the challenges of efficiently removing specific data from ML models without the need for expensive and time-consuming full retraining. This paper presents the various techniques and approaches to IU. It explores the challenges faced in designing and implementing IU mechanisms. Datasets and metrics for evaluating the performance of unlearning techniques are discussed as well. Finally, potential solutions to the IU challenges alongside future research directions are offered. This survey provides valuable insights for researchers and practitioners seeking to understand the current landscape of IU and its potential for enhancing privacy-preserving intelligent systems.

Paper Structure

This paper contains 39 sections, 4 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Overview
  3. Key features of Incremental Unlearning
  4. Techniques and Approaches
  5. Reinforcement Unlearning
  6. Continual Learning via Selective forgetting
  7. Corrective Unlearning
  8. Federated Unlearning
  9. Advanced Metrics for Evaluating Unlearning and Datasets
  10. Evaluation Metrics
  11. Accuracy
  12. Running Time
  13. Theory-Based Verification
  14. Attack-Based Verification
  15. Forgetting Rate
  16. ...and 24 more sections

Figures (3)

  • Figure 1: Machine Unlearning Overflow li2024overview
  • Figure 2: Incremental Unlearning Taxonomy
  • Figure 3: Incremental Unlearning Challenges

Theorems & Definitions (2)

  • Definition 1
  • Definition 2