kennygeemartin/Privacy-Preserving-FL-MobileViT

This study presents an application-oriented privacy-preserving federated learning framework integrating Mobile Vision Transformer (MobileViT) for chest X-ray classification across simulated Nigerian healthcare environments. Using the Nigeria Chest X-ray Dataset (2,600 radiologist-labelled images across four classes) # Privacy-preserving federated MobileViT for Nigerian chest X-rays This repository downloads the public Kaggle dataset, creates reproducible 70/15/15 stratified splits, partitions the training set across five simulated hospitals with a Dirichlet distribution, trains MobileViT-XS using FedAvg, and exports an inference-ready model plus evaluation results. ## Quick start ```powershell python -m pip install -r requirements.txt python train.py download python train.py inspect python train.py train --mode federated ``` The full paper configuration is in `config.yaml`. For a quick end-to-end check: ```powershell python train.py train --mode federated --rounds 1 --local-epochs 1 --max-images 80 --no-pretrained ``` Other experiment modes are `centralized` and `local`. Results are written under `artifacts/ /`, including `best_model.pt`, `model_scripted.pt`, `metrics.json`, `classification_report.csv`, `confusion_matrix.csv`, split manifests, client partitions, and training history. > This is research software, not a medical device. Predictions require clinical > validation and must not be used as a substitute for qualified diagnosis. ## System utility interface The dashboard and model-serving application are in `system-utility-interface`. It reproduces the five-hospital FL workflow and connects uploaded X-rays to the latest exported MobileViT model. See `system-utility-interface/README.md` for launch instructions.