# Cross-Domain Federated Learning in Medical Imaging

arXiv: https://arxiv.org/abs/2112.10001 · HTML: https://mlsanity.com/paper/2112.10001/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 18 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Privacy-preserving Federated Brain Tumour Segmentation](https://mlsanity.com/paper/1910.00962/) (2019)
- [Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge](https://mlsanity.com/paper/2101.07511/) (2021)
- [Multi-task Federated Learning for Heterogeneous Pancreas Segmentation](https://mlsanity.com/paper/2108.08537/) (2021)

## Built on by (most cited first, 6 of 6)

- [Multimodal Federated Learning in Healthcare: a Review](https://mlsanity.com/paper/2310.09650/) (2023)
- [A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation](https://mlsanity.com/paper/2306.08720/) (2023)
- [Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice](https://mlsanity.com/paper/2510.12595/) (2025)
- [BenchMD: A Benchmark for Unified Learning on Medical Images and Sensors](https://mlsanity.com/paper/2304.08486/) (2023)
- [Knowledge-Guided Data-Centric AI in Healthcare: Progress, Shortcomings, and Future Directions](https://mlsanity.com/paper/2212.13591/) (2022)
- [SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations](https://mlsanity.com/paper/2301.07074/) (2023)

## Abstract (arXiv)

Federated learning is increasingly being explored in the field of medical imaging to train deep learning models on large scale datasets distributed across different data centers while preserving privacy by avoiding the need to transfer sensitive patient information. In this manuscript, we explore federated learning in a multi-domain, multi-task setting wherein different participating nodes may contain datasets sourced from different domains and are trained to solve different tasks. We evaluated cross-domain federated learning for the tasks of object detection and segmentation across two different experimental settings: multi-modal and multi-organ. The result from our experiments on cross-domain federated learning framework were very encouraging with an overlap similarity of 0.79 for organ localization and 0.65 for lesion segmentation. Our results demonstrate the potential of federated learning in developing multi-domain, multi-task deep learning models without sharing data from different domains.