This project implements 6 different privacy-preserving collaborative learning methods for training multi-institutional ECG classification models.
A federated learning project
├── LICENSE <- Open-source license if one is chosen
├── Makefile <- Makefile with convenience commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
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├── docs <- A default mkdocs project; see mkdocs.org for details
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├── models <- Trained and serialized models, model predictions, or model summaries
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├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
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├── pyproject.toml <- Project configuration file with package metadata for src
│ and configuration for tools like black
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├── references <- Data dictionaries, manuals, and all other explanatory materials.
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├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
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├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
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├── setup.cfg <- Configuration file for flake8
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└── src <- Source code for use in this project.
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├── __init__.py <- Makes src a Python module
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├── centres <- Scripts to download or generate data
│ └── centre.py
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└── models <- Scripts to train models and then use trained models to make
predictions