# Machine Unlearning: A Survey

arXiv: https://arxiv.org/abs/2306.03558 · HTML: https://mlsanity.com/paper/2306.03558/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Machine unlearning, one of 12 directions in LLM safety and security.
- Builds on 70 of its 129 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Placed in

- LLM safety and security / [Machine unlearning](https://mlsanity.com/safety/machine-unlearning/)

## Builds on (most cited first, 30 of 70)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://mlsanity.com/paper/1806.07572/) (2018)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks](https://mlsanity.com/paper/1911.04933/) (2019)
- [Certified Data Removal from Machine Learning Models](https://mlsanity.com/paper/1911.03030/) (2019)
- [Making AI Forget You: Data Deletion in Machine Learning](https://mlsanity.com/paper/1907.05012/) (2019)
- [New insights and perspectives on the natural gradient method](https://mlsanity.com/paper/1412.1193/) (2014)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Remember What You Want to Forget: Algorithms for Machine Unlearning](https://mlsanity.com/paper/2103.03279/) (2021)
- [Amnesiac Machine Learning](https://mlsanity.com/paper/2010.10981/) (2020)
- [A Survey of Machine Unlearning](https://mlsanity.com/paper/2209.02299/) (2022)
- [Descent-to-Delete: Gradient-Based Methods for Machine Unlearning](https://mlsanity.com/paper/2007.02923/) (2020)
- [Unrolling SGD: Understanding Factors Influencing Machine Unlearning](https://mlsanity.com/paper/2109.13398/) (2021)
- [Approximate Data Deletion from Machine Learning Models](https://mlsanity.com/paper/2002.10077/) (2020)
- [Machine Unlearning of Features and Labels](https://mlsanity.com/paper/2108.11577/) (2021)
- [Fast Yet Effective Machine Unlearning](https://mlsanity.com/paper/2111.08947/) (2021)
- [When Machine Unlearning Jeopardizes Privacy](https://mlsanity.com/paper/2005.02205/) (2020)
- [Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations](https://mlsanity.com/paper/2003.02960/) (2020)
- [Adaptive Machine Unlearning](https://mlsanity.com/paper/2106.04378/) (2021)
- [On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning](https://mlsanity.com/paper/2110.11891/) (2021)
- [Mixed-Privacy Forgetting in Deep Networks](https://mlsanity.com/paper/2012.13431/) (2020)
- [DeltaGrad: Rapid retraining of machine learning models](https://mlsanity.com/paper/2006.14755/) (2020)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [Graph Unlearning](https://mlsanity.com/paper/2103.14991/) (2021)

## Built on by (most cited first, 19 of 19)

- [Position: LLM Unlearning Benchmarks are Weak Measures of Progress](https://mlsanity.com/paper/2410.02879/) (2024)
- [Zero-Shot Class Unlearning in CLIP with Synthetic Samples](https://mlsanity.com/paper/2407.07485/) (2024)
- [A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks](https://mlsanity.com/paper/2406.06186/) (2024)
- [Deep Unlearn: Benchmarking Machine Unlearning for Image Classification](https://mlsanity.com/paper/2410.01276/) (2024)
- [Edge Unlearning is Not "on Edge"! An Adaptive Exact Unlearning System on Resource-Constrained Devices](https://mlsanity.com/paper/2410.10128/) (2024)
- [FUNU: Boosting Machine Unlearning Efficiency by Filtering Unnecessary Unlearning](https://mlsanity.com/paper/2501.16614/) (2025)
- [Fast-NTK: Parameter-Efficient Unlearning for Large-Scale Models](https://mlsanity.com/paper/2312.14923/) (2023)
- [Auditing Approximate Machine Unlearning for Differentially Private Models](https://mlsanity.com/paper/2508.18671/) (2025)
- [Quantum-Inspired Audio Unlearning: Towards Privacy-Preserving Voice Biometrics](https://mlsanity.com/paper/2507.22208/) (2025)
- [Machine Unlearning in Hyperbolic vs. Euclidean Multimodal Contrastive Learning: Adapting Alignment Calibration to MERU](https://mlsanity.com/paper/2503.15166/) (2025)
- [Efficient Machine Unlearning by Model Splitting and Core Sample Selection](https://mlsanity.com/paper/2505.07026/) (2025)
- [EFU: Enforcing Federated Unlearning via Functional Encryption](https://mlsanity.com/paper/2508.07873/) (2025)
- [Pre-Forgettable Models: Prompt Learning as a Native Mechanism for Unlearning](https://mlsanity.com/paper/2509.15230/) (2025)
- [Improving Unlearning with Model Updates Probably Aligned with Gradients](https://mlsanity.com/paper/2511.02435/) (2025)
- [Machine Unlearning using a Multi-GAN based Model](https://mlsanity.com/paper/2407.18467/) (2024)
- [Machine Unlearning for Speaker-Agnostic Detection of Gender-Based Violence Condition in Speech](https://mlsanity.com/paper/2411.18177/) (2024)
- [Quotation-Based Data Retention Mechanism for Data Privacy in LLM-Empowered Network Services](https://mlsanity.com/paper/2503.23001/) (2025)
- [Graph Unlearning: Efficient Node Removal in Graph Neural Networks](https://mlsanity.com/paper/2509.04785/) (2025)
- [Membership Inference Attacks for Retrieval Based In-Context Learning for Document Question Answering](https://mlsanity.com/paper/2605.04116/) (2026)

## Abstract (arXiv)

Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision, speech recognition, medical diagnosis, and more. Yet a special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. At the same time, this ambitious problem has led to numerous research efforts aimed at confronting its challenges. To the best of our knowledge, no study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. Accordingly, with this survey, we aim to capture the key concepts of unlearning techniques. The existing solutions are classified and summarized based on their characteristics within an up-to-date and comprehensive review of each category's advantages and limitations. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.