# FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis

arXiv: https://arxiv.org/abs/2201.08953 · HTML: https://mlsanity.com/paper/2201.08953/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 47 references that are in the map.
- First posted on arXiv 22 Jan 2022.

## Builds on (most cited first, 22 of 22)

- [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)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Which Training Methods for GANs do actually Converge?](https://mlsanity.com/paper/1801.04406/) (2018)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1802.01221/) (2018)
- [GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators](https://mlsanity.com/paper/2006.08265/) (2020)
- [Missing MRI Pulse Sequence Synthesis using Multi-Modal Generative Adversarial Network](https://mlsanity.com/paper/1904.12200/) (2019)
- [Generative Models for Effective ML on Private, Decentralized Datasets](https://mlsanity.com/paper/1911.06679/) (2019)
- [Breaking the Dilemma of Medical Image-to-image Translation](https://mlsanity.com/paper/2110.06465/) (2021)
- [Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/2003.00273/) (2020)
- [G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators](https://mlsanity.com/paper/1906.09338/) (2019)
- [Multi-Domain Image Completion for Random Missing Input Data](https://mlsanity.com/paper/2007.05534/) (2020)
- [Cascaded V-Net using ROI masks for brain tumor segmentation](https://mlsanity.com/paper/1812.11588/) (2018)
- [Segmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization](https://mlsanity.com/paper/2102.06315/) (2021)
- [A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation](https://mlsanity.com/paper/2107.11945/) (2021)
- [Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth](https://mlsanity.com/paper/2107.05482/) (2021)
- [Federated CycleGAN for Privacy-Preserving Image-to-Image Translation](https://mlsanity.com/paper/2106.09246/) (2021)

## Built on by (most cited first, 21 of 21)

- [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023)
- [FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients](https://mlsanity.com/paper/2401.02433/) (2023)
- [One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis](https://mlsanity.com/paper/2207.06509/) (2022)
- [From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare](https://mlsanity.com/paper/2409.09727/) (2024)
- [Multimodal Federated Learning in Healthcare: a Review](https://mlsanity.com/paper/2310.09650/) (2023)
- [Cross-Modality Neuroimage Synthesis: A Survey](https://mlsanity.com/paper/2202.06997/) (2022)
- [3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes](https://mlsanity.com/paper/2406.05421/) (2024)
- [FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis](https://mlsanity.com/paper/2412.06690/) (2024)
- [A Systematic Review of Federated Generative Models](https://mlsanity.com/paper/2405.16682/) (2024)
- [Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes](https://mlsanity.com/paper/2406.11659/) (2024)
- [FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss](https://mlsanity.com/paper/2201.12589/) (2022)
- [Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific Segmentation](https://mlsanity.com/paper/2404.06240/) (2024)
- [ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis](https://mlsanity.com/paper/2502.12180/) (2025)
- [A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck](https://mlsanity.com/paper/2506.08654/) (2025)
- [Conformalised data synthesis](https://mlsanity.com/paper/2312.08999/) (2023)
- [CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AI](https://mlsanity.com/paper/2402.19105/) (2024)
- [Improving Multi-Center Generalizability of GAN-Based Fat Suppression using Federated Learning](https://mlsanity.com/paper/2404.07374/) (2024)
- [Brain Storm Optimization Based Swarm Learning for Diabetic Retinopathy Image Classification](https://mlsanity.com/paper/2404.15585/) (2024)
- [Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead](https://mlsanity.com/paper/2502.06349/) (2025)
- [Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research](https://mlsanity.com/paper/2512.03054/) (2025)
- [FedCoRe: Target-Adaptive Completion for Missing Modalities in Healthcare Federated Learning](https://mlsanity.com/paper/2608.18311/) (2026)

## Abstract (arXiv)

Utilizing multi-modal neuroimaging data has been proved to be effective to investigate human cognitive activities and certain pathologies. However, it is not practical to obtain the full set of paired neuroimaging data centrally since the collection faces several constraints, e.g., high examination cost, long acquisition time, and image corruption. In addition, these data are dispersed into different medical institutions and thus cannot be aggregated for centralized training considering the privacy issues. There is a clear need to launch a federated learning and facilitate the integration of the dispersed data from different institutions. In this paper, we propose a new benchmark for federated domain translation on unsupervised brain image synthesis (termed as FedMed-GAN) to bridge the gap between federated learning and medical GAN. FedMed-GAN mitigates the mode collapse without sacrificing the performance of generators, and is widely applied to different proportions of unpaired and paired data with variation adaptation property. We treat the gradient penalties by federally averaging algorithm and then leveraging differential privacy gradient descent to regularize the training dynamics. A comprehensive evaluation is provided for comparing FedMed-GAN and other centralized methods, which shows the new state-of-the-art performance by our FedMed-GAN. Our code has been released on the website: https://github.com/M-3LAB/FedMed-GAN