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ML examples: - credit_scoring: Credit risk prediction with HMEQ data - disease_prediction: Healthcare ML on encrypted records - cifar: Encrypted image classification - federated_learning: Distributed FHE training - hybrid_model: Mixed FHE/plaintext inference Deployment: - breast_cancer: Cancer detection API - sentiment_analysis: Encrypted NLP - server: FHE inference server Blockchain: - hardhat-template: Solidity + FHE - react-template: Full dApp with FHE
Healthcare diagnosis
Give a diagnosis using FHE to preserve the privacy of the patient. We show how to train several models using Torus ML and choose the one that provides the best accuracy with the lowest inference time. Finally, we export the model in order to use it in the Hugging Face space which provides a live interactive demo this model in use.
Installation
- First, create a virtual env and activate it:
python3.8 -m venv .venv
source .venv/bin/activate
- Then, install required packages:
pip3 install -U pip wheel setuptools --ignore-installed
pip3 install -r requirements.txt --ignore-installed