# Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects

arXiv: https://arxiv.org/abs/2403.08254 · HTML: https://mlsanity.com/paper/2403.08254/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Placed in Machine unlearning, one of 12 directions in LLM safety and security.
- First posted on arXiv 13 Mar 2024.

## Placed in

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

## Built on by (most cited first, 30 of 32)

- [Threats, Attacks, and Defenses in Machine Unlearning: A Survey](https://mlsanity.com/paper/2403.13682/) (2024)
- [Towards Reliable Forgetting: A Survey on Machine Unlearning Verification](https://mlsanity.com/paper/2506.15115/) (2025)
- [A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions](https://mlsanity.com/paper/2412.12836/) (2024)
- [Position: Bridge the Gaps between Machine Unlearning and AI Regulation](https://mlsanity.com/paper/2502.12430/) (2025)
- [Certified Data Removal Under High-dimensional Settings](https://mlsanity.com/paper/2505.07640/) (2025)
- [POUR: A Provably Optimal Method for Unlearning Representations via Neural Collapse](https://mlsanity.com/paper/2511.19339/) (2025)
- [Forgetting: A New Mechanism Towards Better Large Language Model Fine-tuning](https://mlsanity.com/paper/2508.04329/) (2025)
- [CoUn: Empowering Machine Unlearning via Contrastive Learning](https://mlsanity.com/paper/2509.16391/) (2025)
- [Model Inversion Attack against Federated Unlearning](https://mlsanity.com/paper/2502.14558/) (2025)
- [Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2502.16708/) (2025)
- [ZK-APEX: Zero-Knowledge Approximate Personalized Unlearning with Executable Proofs](https://mlsanity.com/paper/2512.09953/) (2025)
- [CUFG: Curriculum Unlearning Guided by the Forgetting Gradient](https://mlsanity.com/paper/2509.14633/) (2025)
- [Beyond Sharp Minima: Robust LLM Unlearning via Feedback-Guided Multi-Point Optimization](https://mlsanity.com/paper/2509.20230/) (2025)
- [Review of Digital Asset Development with Graph Neural Network Unlearning](https://mlsanity.com/paper/2409.18455/) (2024)
- [TLXML: Task-Level Explanation of Meta-Learning via Influence Functions](https://mlsanity.com/paper/2501.14271/) (2025)
- [Unlearning Algorithmic Biases over Graphs](https://mlsanity.com/paper/2505.14945/) (2025)
- [Data Augmentation Improves Machine Unlearning](https://mlsanity.com/paper/2508.18502/) (2025)
- [Towards Verifiable Federated Unlearning: Framework, Challenges, and The Road Ahead](https://mlsanity.com/paper/2510.00833/) (2025)
- [Supervised Contrastive Machine Unlearning of Background Bias in Sonar Image Classification with Fine-Grained Explainable AI](https://mlsanity.com/paper/2512.01291/) (2025)
- [Shadow Unlearning: A Neuro-Semantic Approach to Fidelity-Preserving Faceless Forgetting in LLMs](https://mlsanity.com/paper/2601.04275/) (2026)
- [f-FUM: Federated Unlearning via min--max and f-divergence](https://mlsanity.com/paper/2602.06187/) (2026)
- [Client-Verifiable and Efficient Federated Unlearning in Low-Altitude Wireless Networks](https://mlsanity.com/paper/2603.29688/) (2026)
- [ArmSSL: Adversarial Robust Black-Box Watermarking for Self-Supervised Learning Pre-trained Encoders](https://mlsanity.com/paper/2604.22550/) (2026)
- [Does Machine Unlearning Preserve Clinical Safety? A Risk Analysis for Medical Image Classification](https://mlsanity.com/paper/2604.23854/) (2026)
- [FedUP: One-Shot Federated Unlearning via Centroid-Guided Plug-in Filters](https://mlsanity.com/paper/2606.24113/) (2026)
- [DFMU: Data-Frugal Machine Unlearning](https://mlsanity.com/paper/2606.25410/) (2026)
- [DECAF: De-Clustering for Adaptive Representational Unlearning](https://mlsanity.com/paper/2607.23934/) (2026)
- [On-the-go Forgetting without Explicit Unlearning via ERASE](https://mlsanity.com/paper/2609.05966/) (2026)
- [CUNO: Curriculum and Preference Optimization for Stable Graph Unlearning under Mass Deletion](https://mlsanity.com/paper/2609.08244/) (2026)
- [Modeling Deletion Requests in Machine Unlearning](https://mlsanity.com/paper/2610.04310/) (2026)

## Abstract (arXiv)

Personal digital data is a critical asset, and governments worldwide have enforced laws and regulations to protect data privacy. Data users have been endowed with the right to be forgotten of their data. In the course of machine learning (ML), the forgotten right requires a model provider to delete user data and its subsequent impact on ML models upon user requests. Machine unlearning emerges to address this, which has garnered ever-increasing attention from both industry and academia. While the area has developed rapidly, there is a lack of comprehensive surveys to capture the latest advancements. Recognizing this shortage, we conduct an extensive exploration to map the landscape of machine unlearning including the (fine-grained) taxonomy of unlearning algorithms under centralized and distributed settings, debate on approximate unlearning, verification and evaluation metrics, challenges and solutions for unlearning under different applications, as well as attacks targeting machine unlearning. The survey concludes by outlining potential directions for future research, hoping to serve as a guide for interested scholars.