Summary of In-depth Analysis Of Privacy Threats in Federated Learning For Medical Data, by Badhan Chandra Das et al.
In-depth Analysis of Privacy Threats in Federated Learning for Medical Data
by Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu
First submitted to arxiv on: 27 Sep 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 This paper investigates the privacy risks and mitigation strategies in federated learning, a machine learning technique used for analyzing medical images while safeguarding sensitive patient data. The authors propose MedPFL, a framework for analyzing privacy risks and developing effective mitigation strategies. They demonstrate severe privacy risks in processing medical images through empirical analysis, where adversaries can reconstruct private medical images by performing privacy attacks. Additionally, they show that adding random noises may not always be effective in protecting medical images against privacy attacks. The paper discusses unique research questions related to the privacy protection of medical data and conducts extensive experiments on several benchmark medical image datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about keeping patient medical information private while using a special kind of computer learning called federated learning. Right now, this technique can be used to analyze medical images without revealing sensitive patient data. However, some studies have shown that the default settings for this technique might accidentally expose private training data. This paper tries to figure out how big of a problem this is and what we can do about it. The authors create a framework called MedPFL to help understand these risks and find ways to keep medical information safe. They also test their ideas on real medical image datasets. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
