DynamicFL

Towards Fairness-aware and Privacy-preserving Enhanced Collaborative Learning for Healthcare [ Under Review]

Getting Started

1. Data

This project uses three publicly available datasets. Please follow the steps below to download and prepare the datasets:

Dataset 1: ChestXray

1. Download the dataset from Kaggle.
   
2. Create a folder named data in the root directory of this project.
   
3. Extract the downloaded dataset into the data/covid directory.
   
   • After extraction, the folder structure should look like:
     data/ COVID-19/ COVID/ Normal/ Lung_Opacity/ Viral Pneumonia/
     
4. Inside each of the class folders (COVID, NORMAL, LUNG_OPACITY, VIRAL_PNEUMONIA), delete the masks subfolder because this project focuses only on classification tasks and does not involve segmentation.
   

---

Dataset 2 & Dataset 3

1. Download the second and third datasets from Zenodo.
   
2. Extract these datasets into the data directory.

2. Install Dependencies

To set up the environment, ensure you have Python and Anaconda installed, then install the required libraries using the requirements.txt file, 12.1 cuda environment is recommended:

```bash conda create -n dyfl python=3.9 conda activate dyfl pip install -r requirements.txt pip install --upgrade wandb protobuf

```

3. Usage

3.1 Training with Different ViT Architectures

You can train the model with different ViT (Vision Transformer) architectures by specifying the --model parameter. Below are some examples:

```python

python dyflvit.py --dataset bloodcell --nodenum 30 --model Vit_tiny --device cuda:0

python dyflvit.py --dataset bloodcell --nodenum 30 --model Vit_small --device cuda:0

python dyflvit.py --dataset bloodcell --nodenum 30 --model Vit_Base --device cuda:0

python dyflvit.py --dataset bloodcell --nodenum 30 --model Vit_1B --device cuda:0

```

3.2 Training with Non-IID Data Partitions

You can simulate non-IID (non-Independent and Identically Distributed) data by using the --partition parameter and setting the Dirichlet distribution parameter (--dir). Below are some examples:

```python

python dyflvit.py --dataset bloodcell --nodenum 30 --model Vit_Base --device cuda:0 --partition dir --dir 0.1

python dyflvit.py --dataset bloodcell --nodenum 30 --model Vit_Base --device cuda:0 --partition dir --dir 0.3

python dyflvit.py --dataset bloodcell --nodenum 30 --model Vit_Base --device cuda:0 --partition dir --dir 0.5

python dyflvit.py --dataset bloodcell --nodenum 30 --model Vit_Base --device cuda:0 --partition dir --dir 0.7

python dyflvit.py --dataset bloodcell --nodenum 30 --model Vit_Base --device cuda:0 --partition dir --dir 0.9 ```

3.3 Training with Different Device Ratios

You can allocate computational resources across devices in specific ratios using the --device_ratio parameter. Below are some examples:

```python

python dyflvit.py --dataset bloodcell --nodenum 30 --model VitBase --device cuda:0 --deviceratio 7:2:1

python dyflvit.py --dataset bloodcell --nodenum 30 --model VitBase --device cuda:0 --deviceratio 5:2:3

python dyflvit.py --dataset bloodcell --nodenum 30 --model VitBase --device cuda:0 --deviceratio 4:1:5

python dyflvit.py --dataset bloodcell --nodenum 30 --model VitBase --device cuda:0 --deviceratio 4:3:3

python dyflvit.py --dataset bloodcell --nodenum 30 --model VitBase --device cuda:0 --deviceratio 3:6:1

```

3.4 Training with Different Device Counts

You can vary the number of devices (or nodes) in your distributed setup using the --node_num parameter. Below are some examples:

```python

python dyflvit.py --dataset bloodcell --nodenum 90 --model Vit_Base --device cuda:0

python dyflvit.py --dataset bloodcell --nodenum 180 --model Vit_Base --device cuda:0

python dyflvit.py --dataset bloodcell --nodenum 360 --model Vit_Base --device cuda:0

```

3.5 Running on CPU Only (High Memory Requirement)

If you want to run only on CPU, you can set the --device parameter to cpu. Note: Running on CPU requires very high RAM, especially for larger ViT models. It is recommended to use ViT-Tiny for testing on CPU, with at least 128GB of RAM.

```python

python dyflvit.py --dataset bloodcell --device cpu --model vittiny --wandb 0

```

4. Contact

For any questions or issues, please open an issue on the GitHub repository.