Chest X-ray Federated Learning (NIH)

Overview

This repository organizes your Federated Learning experiments on the NIH Chest X-ray dataset. We preserve your original notebook while providing a reproducible project layout and a script export.

• Original notebook: notebooks/NIH_Chest_Xray_Federated_Learning.ipynb
• Exported notebook code: src/notebook_export.py
• Minimal launcher: src/train.py
• Default config: configs/default.yaml

Note: src/notebook_export.py concatenates notebook cells in order. For long-term maintenance, consider refactoring into modules such as datasets.py, models.py, and train.py.

Quickstart

git clone https://github.com/<YOUR-USER-OR-ORG>/chestxray-federated-learning.git
cd chestxray-federated-learning
bash scripts/run_local.sh

Or manually:

python3 -m venv .venv && source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
python src/train.py --workdir .

Dataset

• The experiments use the NIH Chest X-ray (ChestX-ray14) dataset.
• Download/preprocessing follows the logic embedded in the notebook. (Some cells may rely on Google Colab; adjust or skip those when running locally.)
• If you reorganize the data into an ImageFolder layout, update paths in configs/default.yaml accordingly.

Environment

requirements.txt was generated based on actual imports detected in the notebook.

• Please install PyTorch / CUDA versions appropriate for your machine (e.g., CUDA 12.x).

Citation

If this code or its results are useful for your research, please cite this repository via CITATION.cff.

License

MIT License (see LICENSE)