Summary of Fishers Harvest Parallel Unlearning in Inherited Model Networks, by Xiao Liu et al.
Fishers Harvest Parallel Unlearning in Inherited Model Networks
by Xiao Liu, Mingyuan Li, Xu Wang, Guangsheng Yu, Wei Ni, Lixiang Li, Haipeng Peng, Renping Liu
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 This paper proposes a novel unlearning framework that enables parallel unlearning among models with complex inheritance relationships, a common challenge in various learning frameworks. The Unified Model Inheritance Graph (UMIG) and Fisher Inheritance Unlearning (FIUn) algorithm are key enablers of this framework. FIUn utilizes the Fisher Information Matrix (FIM) to pinpoint impacted parameters in inherited models, breaking sequential dependencies and reducing computational overhead. Experiments confirm the effectiveness of this unlearning framework, achieving complete unlearning with 0% accuracy for unlearned labels while maintaining high accuracy for retained labels on average. This framework accelerates unlearning by 99% compared to alternative methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about a new way to help machines “unlearn” what they’ve learned when something goes wrong. When a machine has learned many things from different sources, it’s hard for it to forget the bad information without forgetting the good too. The authors of this paper created a new tool that helps machines unlearn in a way that keeps the good information and gets rid of the bad. They tested their tool on various problems and found that it worked very well, making it possible for machines to “unlearn” quickly and accurately. |
