Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions

TL;DR

This paper surveys incremental unlearning in machine learning, discussing techniques, challenges, evaluation metrics, and future directions to improve data privacy and model efficiency.

Contribution

It provides a comprehensive overview of incremental unlearning methods, challenges, and evaluation approaches, highlighting future research opportunities in privacy-preserving ML.

Findings

Various IU techniques are categorized and analyzed.

Challenges include efficiency, scalability, and accuracy preservation.

Future directions emphasize developing standardized benchmarks and scalable solutions.

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.