# Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy

arXiv: https://arxiv.org/abs/2305.06360 · HTML: https://mlsanity.com/paper/2305.06360/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Placed in Machine unlearning, one of 12 directions in LLM safety and security.
- Builds on 156 of its 357 references that are in the map.
- First posted on arXiv 10 May 2023.

## Placed in

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

## Builds on (most cited first, 30 of 156)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks](https://mlsanity.com/paper/1911.04933/) (2019)
- [Knowledge Neurons in Pretrained Transformers](https://mlsanity.com/paper/2104.08696/) (2021)
- [Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference](https://mlsanity.com/paper/1810.11910/) (2018)
- [Pruning neural networks without any data by iteratively conserving synaptic flow](https://mlsanity.com/paper/2006.05467/) (2020)
- [Making AI Forget You: Data Deletion in Machine Learning](https://mlsanity.com/paper/1907.05012/) (2019)
- [Backdoor Learning: A Survey](https://mlsanity.com/paper/2007.08745/) (2020)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Remember What You Want to Forget: Algorithms for Machine Unlearning](https://mlsanity.com/paper/2103.03279/) (2021)
- [Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks](https://mlsanity.com/paper/2101.05930/) (2021)

## Built on by (most cited first, 30 of 51)

- [Eight Methods to Evaluate Robust Unlearning in LLMs](https://mlsanity.com/paper/2402.16835/) (2024)
- [Large Language Model Unlearning via Embedding-Corrupted Prompts](https://mlsanity.com/paper/2406.07933/) (2024)
- [Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset](https://mlsanity.com/paper/2411.03554/) (2024)
- [Learning to Unlearn while Retaining: Combating Gradient Conflicts in Machine Unlearning](https://mlsanity.com/paper/2503.06339/) (2025)
- [Is Retain Set All You Need in Machine Unlearning? Restoring Performance of Unlearned Models with Out-Of-Distribution Images](https://mlsanity.com/paper/2404.12922/) (2024)
- [Targeted Unlearning with Single Layer Unlearning Gradient](https://mlsanity.com/paper/2407.11867/) (2024)
- [Data Unlearning in Diffusion Models](https://mlsanity.com/paper/2503.01034/) (2025)
- [A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks](https://mlsanity.com/paper/2406.06186/) (2024)
- [FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning](https://mlsanity.com/paper/2309.10283/) (2023)
- [DUCK: Distance-based Unlearning via Centroid Kinematics](https://mlsanity.com/paper/2312.02052/) (2023)
- [Deep Unlearn: Benchmarking Machine Unlearning for Image Classification](https://mlsanity.com/paper/2410.01276/) (2024)
- [GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks](https://mlsanity.com/paper/2312.07861/) (2023)
- [RAZOR: Ratio-Aware Layer Editing for Targeted Unlearning in Vision Transformers and Diffusion Models](https://mlsanity.com/paper/2603.14819/) (2026)
- [Model Integrity when Unlearning with T2I Diffusion Models](https://mlsanity.com/paper/2411.02068/) (2024)
- [A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions](https://mlsanity.com/paper/2412.12836/) (2024)
- [DualOptim: Enhancing Efficacy and Stability in Machine Unlearning with Dual Optimizers](https://mlsanity.com/paper/2504.15827/) (2025)
- [FG-OrIU: Towards Better Forgetting via Feature-Gradient Orthogonality for Incremental Unlearning](https://mlsanity.com/paper/2601.13578/) (2026)
- [Privacy-Preserving Federated Unlearning with Certified Client Removal](https://mlsanity.com/paper/2404.09724/) (2024)
- [Verifying Machine Unlearning with Explainable AI](https://mlsanity.com/paper/2411.13332/) (2024)
- [Split Unlearning](https://mlsanity.com/paper/2308.10422/) (2023)
- [Composable Interventions for Language Models](https://mlsanity.com/paper/2407.06483/) (2024)
- [How Secure is Forgetting? Linking Machine Unlearning to Machine Learning Attacks](https://mlsanity.com/paper/2503.20257/) (2025)
- [Unlearning's Blind Spots: Over-Unlearning and Prototypical Relearning Attack](https://mlsanity.com/paper/2506.01318/) (2025)
- [Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods](https://mlsanity.com/paper/2508.12730/) (2025)
- [CoUn: Empowering Machine Unlearning via Contrastive Learning](https://mlsanity.com/paper/2509.16391/) (2025)
- [Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions](https://mlsanity.com/paper/2309.10980/) (2023)
- [FaLW: A Forgetting-aware Loss Reweighting for Long-tailed Unlearning](https://mlsanity.com/paper/2601.18650/) (2026)
- [Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2502.16708/) (2025)
- [ZIUM: Zero-Shot Intent-Aware Adversarial Attack on Unlearned Models](https://mlsanity.com/paper/2507.21985/) (2025)
- [Feature-Selective Representation Misdirection for Machine Unlearning](https://mlsanity.com/paper/2512.16297/) (2025)

## Abstract (arXiv)

Machine unlearning (MU) is gaining increasing attention due to the need to remove or modify predictions made by machine learning (ML) models. While training models have become more efficient and accurate, the importance of unlearning previously learned information has become increasingly significant in fields such as privacy, security, and fairness. This paper presents a comprehensive survey of MU, covering current state-of-the-art techniques and approaches, including data deletion, perturbation, and model updates. In addition, commonly used metrics and datasets are also presented. The paper also highlights the challenges that need to be addressed, including attack sophistication, standardization, transferability, interpretability, training data, and resource constraints. The contributions of this paper include discussions about the potential benefits of MU and its future directions. Additionally, the paper emphasizes the need for researchers and practitioners to continue exploring and refining unlearning techniques to ensure that ML models can adapt to changing circumstances while maintaining user trust. The importance of unlearning is further highlighted in making Artificial Intelligence (AI) more trustworthy and transparent, especially with the increasing importance of AI in various domains that involve large amounts of personal user data.