HybridVFL-HAM10K: Fairness-Aware Vertical Federated Learning for Skin Cancer Detection

A modular Python implementation of FairVFL adapted for the HAM10000 skin lesion dataset.
This project provides a research-ready codebase for exploring fairness and privacy in vertical federated learning using both image and tabular medical data and demonstrates metrics and plots on a modern front-end dashboard.

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Project Structure

• main.py — Entry point for training and evaluation
• data.py — Data loading and preprocessing (HAM10000 images + metadata)
• model.py — Model architectures (CNN, tabular encoder, fairness heads)
• train.py — Training routines
• evaluate.py — Evaluation and fairness audit routines
• dashboard.py — Flask/SocketIO backend for dashboard live metrics
• templates/dashboard.html — Dashboard front-end template
• statics/dashboard.css — Dashboard CSS styles
• statics/dashboard.js — Dashboard interactive JS
• requirements.txt — Python dependencies
• .gitignore — Standard ignores for Python and data
• /data/ — Place the HAM10000 dataset here

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Setup

1. Clone this repository.

2. Download the HAM10000 dataset from here
   Place the extracted folders and CSV in your /data directory as follows:

   data/
   ├── HAM10000_images_part_1/
   │   ├── ISIC_0024306.jpg
   │   ├── ...
   ├── HAM10000_images_part_2/
   │   ├── ISIC_0032012.jpg
   │   ├── ...
   ├── HAM10000_metadata.csv
   

3. Install dependencies:

   pip install -r requirements.txt

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Run

Run the Project with Dashboard

To launch the live dashboard and start FairVFL training with real-time metrics, run:

python dashboard.py

It opens http://localhost:5050 in your browser to view the dashboard. Dashboard front-end is served via Flask/SocketIO and will automatically update with training progress.

Run CLI only

If you only want to run the core training and evaluation via CLI, use:

python main.py

Note: For faster testing, you can reduce the percentage of data used by setting the PERCENTAGE variable in main.py to a lower value (e.g., PERCENTAGE = 0.1 for 10% of the data). This will significantly speed up training and evaluation, making it ideal for quick experiments or debugging.**

PERCENTAGE = 0.1  # Use only 10% of data for fast testing