Summary of Decentralised, Collaborative, and Privacy-preserving Machine Learning For Multi-hospital Data, by Congyu Fang et al.
Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data
by Congyu Fang, Adam Dziedzic, Lin Zhang, Laura Oliva, Amol Verma, Fahad Razak, Nicolas Papernot, Bo Wang
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 approach to machine learning (ML) for medical data analysis, addressing the challenge of collaborative training without compromising patient privacy. The Decentralized, Collaborative, and Privacy-preserving ML for Multi-Hospital Data (DeCaPH) framework enables multiple parties to jointly train an ML model using their private datasets without direct sharing or compromising patient confidentiality. DeCaPH offers improved utility-privacy trade-offs and enhanced model generalizability, demonstrated on three real-world medical tasks: mortality prediction, cell-type classification, and pathology identification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in medicine! Right now, it’s hard to share medical data between hospitals because of privacy concerns. This makes it difficult for doctors to train super accurate machine learning models using lots of different data. The solution is called DeCaPH (say “de-caf”). It lets different hospitals work together to train a model without sharing their private data or compromising patient privacy. This means better predictions and more accurate diagnoses. The paper shows that DeCaPH works really well on three important medical tasks: predicting when patients might die, identifying what’s wrong with cells, and recognizing diseases from X-rays. |
Keywords
* Artificial intelligence * Classification * Machine learning
