Summary of Unlearning During Learning: An Efficient Federated Machine Unlearning Method, by Hanlin Gu et al.
Unlearning during Learning: An Efficient Federated Machine Unlearning Method
by Hanlin Gu, Gongxi Zhu, Jie Zhang, Xinyuan Zhao, Yuxing Han, Lixin Fan, Qiang Yang
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 new framework for federated machine unlearning (FMU) called FedAU, which addresses limitations in current FMU approaches by incorporating a lightweight auxiliary unlearning module into the learning process. This approach eliminates the need for additional time-consuming steps and enables concurrent unlearning tasks across multiple clients at various levels of granularity. The proposed method is evaluated on MNIST, CIFAR10, and CIFAR100 datasets, demonstrating effective unlearning with preserved model accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this paper introduces a new way to “forget” unwanted data in distributed machine learning systems. It’s called federated machine unlearning (FMU), and the authors propose a solution called FedAU that makes it faster and more efficient. They tested it on some popular datasets and showed that it works well. |
Keywords
» Artificial intelligence » Machine learning
