FedHarmony

Code implementation for FedHarmony (MICCAI 2022)

This is a working release of the code for FedHarmony Any issues please contact: nicola.dinsdale@cs.ox.ac.uk. Further code will be added in time.

Software Versions

Python 3.6.8

PyTorch 1.10.1

Code supplied was used for the age prediction task but the framework is general. The architecture used needs to have three sections as shown in the figure above:

• Feature extractor
• Label Predictor
• Domain Predictor

Scripts

The training procedure is formed of three distinct phases:

• Stage 1: harmonisation_main --> train the model for each site separately
• Stage 2: fed_equal --> aggregate weights according to FedEqual regime
• Stage 3: get_gaussians --> fit gaussian to data for each site separately

If you use code from this repository please cite the appropriate paper:

FedHarmony:

@article{FEDHARMONY,
  doi = {10.48550/ARXIV.2205.15970},  
  author = {Dinsdale, Nicola K and Jenkinson, Mark and Namburete, Ana IL},
  title = {FedHarmony: Unlearning Scanner Bias with Distributed Data},
  publisher = {arXiv},
  year = {2022},
}

Unlearning Scanner Bias for MRI Harmonisation:

title = {Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal},
journal = {NeuroImage},
volume = {228},
pages = {117689},
year = {2021},
doi = {https://doi.org/10.1016/j.neuroimage.2020.117689},
author = {Nicola K. Dinsdale and Mark Jenkinson and Ana I.L. Namburete}
}