# Federated nnU-Net for privacy-preserving medical image segmentation

## TL;DR

This paper introduces FednnU-Net, a privacy-preserving framework for medical image segmentation using federated learning.

## Contribution

The paper introduces two novel federated learning methodologies, FFE and AsymFedAvg, for decentralized training of the nnU-Net framework.

## Key findings

- FednnU-Net achieves high and consistent performance for breast, cardiac, and fetal segmentation.
- The framework is tested on a multi-modal collection of 6 datasets from 18 institutions.
- The proposed methods enable decentralized training while preserving patient privacy.

## Abstract

The nnU-Net framework has played a crucial role in medical image segmentation and has become the gold standard in multitudes of applications targeting different diseases, organs, and modalities. However, so far it has been used primarily in a centralized approach where the collected data is stored in the same location where nnU-Net is trained. This centralized approach has various limitations, such as potential leakage of sensitive patient information and violation of patient privacy. Federated learning has emerged as a key approach for training segmentation models in a decentralized manner, enabling collaborative development while prioritising patient privacy. In this paper, we propose FednnU-Net, a plug-and-play, federated learning extension of the nnU-Net framework. To this end, we contribute two federated methodologies to unlock decentralized training of nnU-Net, namely, Federated Fingerprint Extraction (FFE) and Asymmetric Federated Averaging (AsymFedAvg). We conduct a comprehensive set of experiments demonstrating high and consistent performance of our methods for breast, cardiac and fetal segmentation based on a multi-modal collection of 6 datasets representing samples from 18 different institutions. To democratize research as well as real-world deployments of decentralized training in clinical centres, we publicly share our framework at https://github.com/faildeny/FednnUNet.