This is the PyTorch implementation of our ICLR 2023 paper: FedFA: Federated Feature Augmentation by Tianfei Zhou and Ender Konukoglu, from ETH Zurich.

• [2023-02-24] Code released with reimplementations of experiments

• python/3.10.4
• pytorch/1.11.0
• cuda/11.3.1
• gcc/6.3.0
• numpy/1.22.4
• scipy/1.8.1
• opencv-python/4.5.5
• wandb (for experiment tracking)

• Office-Caltech 10: https://drive.google.com/file/d/1gKd0M2Z6gdFwHntgsMTq5ZF5tQIg_bUD/view?usp=share_link
• Prostate MRI: https://drive.google.com/file/d/1GHy3hWAIU288Lr-NF80lydoJQmC7zlmk/view?usp=share_link
• DomainNet: TBD

Bash training scripts for prostate_mri and office-caltech are provided in run_scripts. They are written for our cluster, but can be easily adapted for any kind of training environments. For scripts of other datasets, they are very similar with existing ones; or I will update them later.

Specify data path in Line 11 or Line 12 in config/prostate_mri/base.py:

self.DIR_ROOT = os.environ.get('TMPDIR')
self.DIR_DATA = os.path.join(self.DIR_ROOT, 'ProstateMRI')

Enter the directory of shell scripts, e.g., run_scripts/euler/prostate_mri:

cd run_scripts/euler/prostate_mri

Run training:

bash euler_train_fedfa.sh

If you find this work useful, please consider citing:

@inproceedings{zhou2023fedfa,
  title={Fed{FA}: Federated Feature Augmentation},
  author={Tianfei Zhou and Ender Konukoglu},
  booktitle={The Eleventh International Conference on Learning Representations (ICLR)},
  year={2023}
}