# FedFound: Federated Foundation Model for GI Endoscopy Imaging


This repository contains the **PyTorch implementation** of **FedFound**, our federated learning framework for **foundation model pretraining and downstream tasks** in gastrointestinal (GI) endoscopy imaging.  

FedFound enables **federated self-supervised pretraining** of Vision Transformer (ViT)–based foundation models across multiple institutions while preserving data privacy. The learned representations can be fine-tuned for **classification, object detection, and instance segmentation** in GI endoscopy datasets.




##  Quick Start

### 1. Create Conda environment
```bash
conda create --name ffm python=3.8
```

### 2. Install required libraries
```bash
pip install -r requirements.txt
```

### 3. Dataset Preparation
Please follow the instructions in `data/README.md` to organize your dataset before training.

### 4. Run pretraining
The scripts for pretraining and fine-tuning are located in the `/code/fedmae/scripts/endo` folder:

- **SLURM users:**  
  Run `start_multiple_pretraining.sh` to launch federated pretraining across multiple nodes.

- **Local users:**  
  Run individual pretraining scripts such as `fedavg_ssl.sh`, `fedavgm_ssl.sh`, etc. for single-machine experiments.

- **Baseline experiments:**  
  Use `fed_ssl.sh` to run lower- and upper-bound comparisons.

### 5. Finetuning
The scripts for fine-tuning pretrained models are also in the `/code/fed_mae/scripts/endo` folder:

- **SLURM users:**  
  Run `start_multiple_finetuning.sh` to launch fine-tuning for multiple methods.

- **Local users:**  
  Refer to `fed_ssl_finetune_multiple.sh` for individual or single-machine fine-tuning experiments.

## Acknowledgements

This repository builds upon and extends the code from the [SSL-FL repository](https://github.com/rui-yan/SSL-FL) by Rui Yan et al., which provided the starting point for our federated self-supervised learning framework.