Summary of Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects, by Na Li et al.
Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects
by Na Li, Chunyi Zhou, Yansong Gao, Hui Chen, Anmin Fu, Zhi Zhang, Yu Shui
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A comprehensive survey of machine unlearning explores the latest advancements in this rapidly developing field, which addresses the need for deleting user data and its impact on machine learning models upon user requests. The study provides a fine-grained taxonomy of unlearning algorithms under centralized and distributed settings, debates approximate unlearning, and discusses verification and evaluation metrics. Additionally, it highlights challenges and solutions for unlearning in various applications and potential attacks targeting machine unlearning. This survey concludes by outlining future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning (ML) is important for personal digital data protection. A new area called “machine unlearning” helps with this. When someone wants their data deleted, a model provider must erase the user’s data and its impact on ML models. This survey looks at the latest advancements in machine unlearning, including how it works under different settings. It also talks about challenges, solutions, and potential attacks. The goal is to help researchers find new ways to improve machine unlearning. |
Keywords
* Artificial intelligence * Machine learning
