Summary of Streamlined Federated Unlearning: Unite As One to Be Highly Efficient, by Lei Zhou et al.
Streamlined Federated Unlearning: Unite as One to Be Highly Efficient
by Lei Zhou, Youwen Zhu, Qiao Xue, Ji Zhang, Pengfei Zhang
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Recently, federated learning (FL) has been impacted by new privacy requirements imposed by “right to be forgotten” laws and regulations. Researchers have developed federated unlearning (FU) techniques to remove data influence from trained models without retraining from scratch. However, current FU approaches often result in degraded model performance after achieving the goal of data unlearning, requiring additional steps to recover the original performance. Moreover, these methods consume significant computational and storage resources. To address this issue, we propose a streamlined federated unlearning approach (SFU) that effectively removes target data influence while preserving model performance on retained data without degradation. SFU is designed as a practical multi-teacher system guiding the unlearned model through distinct teacher models to achieve both goals. Our approach is computationally and storage-efficient, flexible, and generalizable. We conduct extensive experiments on image and text benchmark datasets, demonstrating that SFU outperforms existing state-of-the-art methods in terms of time and communication efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to delete a memory from your brain without losing all the other memories. That’s basically what this paper is about – finding ways to “unlearn” certain data from a model without ruining its overall performance. Right now, scientists are working on new laws that require more privacy in something called federated learning. This means they need to figure out how to remove unwanted information without re-training the whole model from scratch. The problem is that current methods often make the model perform worse after deleting the unwanted data. To solve this issue, researchers propose a new approach called SFU (streamlined federated unlearning). It’s like having multiple teachers helping you unlearn the bad memory while keeping all the good ones intact. They tested it on some big datasets and found that it works better than existing methods in terms of time and communication efficiency. |
Keywords
» Artificial intelligence » Federated learning
