tayebiarasteh/federated_he
Federated learning with homomorphic encryption enables multiple parties to securely co-train artificial intelligence models in pathology and radiology, reaching state-of-the-art performance with privacy guarantees.
This project helps medical institutions securely collaborate on training advanced AI models for cancer image analysis, specifically in pathology and radiology. It takes decentralized medical image datasets and produces a highly accurate, privacy-preserving AI model for diagnosis and prognosis. Researchers, clinicians, and medical AI developers can use this to improve model performance without compromising patient data.
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Use this if you need to co-train AI models across multiple medical centers using sensitive patient imaging data, but strict privacy regulations prevent direct data sharing.
Not ideal if your data can be shared directly and openly, or if you are not working with medical imaging or other highly sensitive datasets.
Stars
32
Forks
5
Language
Python
License
MIT
Category
Last pushed
Dec 07, 2023
Commits (30d)
0
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