# A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation

arXiv: https://arxiv.org/abs/2306.08720 · HTML: https://mlsanity.com/paper/2306.08720/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 31 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 25 of 25)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [Weight Poisoning Attacks on Pre-trained Models](https://mlsanity.com/paper/2004.06660/) (2020)
- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
- [Federated Optimization:Distributed Optimization Beyond the Datacenter](https://mlsanity.com/paper/1511.03575/) (2015)
- [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016)
- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems](https://mlsanity.com/paper/1907.10456/) (2019)
- [Surgical Data Science -- from Concepts toward Clinical Translation](https://mlsanity.com/paper/2011.02284/) (2020)
- [Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning](https://mlsanity.com/paper/2103.02148/) (2021)
- [Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation](https://mlsanity.com/paper/1702.07841/) (2017)
- [A multi-centre polyp detection and segmentation dataset for generalisability assessment](https://mlsanity.com/paper/2106.04463/) (2021)
- [Siloed Federated Learning for Multi-Centric Histopathology Datasets](https://mlsanity.com/paper/2008.07424/) (2020)
- [Learning Aligned Cross-Modal Representations from Weakly Aligned Data](https://mlsanity.com/paper/1607.07295/) (2016)
- [CD2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning](https://mlsanity.com/paper/2204.03880/) (2022)
- [FedSynth: Gradient Compression via Synthetic Data in Federated Learning](https://mlsanity.com/paper/2204.01273/) (2022)
- [The challenges of deploying artificial intelligence models in a rapidly evolving pandemic](https://mlsanity.com/paper/2005.12137/) (2020)
- [A Novel Domain Adaptation Framework for Medical Image Segmentation](https://mlsanity.com/paper/1810.05732/) (2018)
- [EndoUDA: A modality independent segmentation approach for endoscopy imaging](https://mlsanity.com/paper/2107.05342/) (2021)
- [Cross-Domain Federated Learning in Medical Imaging](https://mlsanity.com/paper/2112.10001/) (2021)
- [Deep fusion of visual signatures for client-server facial analysis](https://mlsanity.com/paper/1611.00142/) (2016)

## Built on by (most cited first, 4 of 4)

- [Spatio-Temporal Representation Decoupling and Enhancement for Federated Instrument Segmentation in Surgical Videos](https://mlsanity.com/paper/2506.23759/) (2025)
- [MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation](https://mlsanity.com/paper/2503.20830/) (2025)
- [Foam Segmentation in Wastewater Treatment Plants: A Federated Learning Approach with Segment Anything Model 2](https://mlsanity.com/paper/2511.08130/) (2025)
- [SurgFed: Language-guided Multi-Task Federated Learning for Surgical Video Understanding](https://mlsanity.com/paper/2603.09496/) (2026)

## Abstract (arXiv)

This paper presents a solution to the cross-domain adaptation problem for 2D surgical image segmentation, explicitly considering the privacy protection of distributed datasets belonging to different centers. Deep learning architectures in medical image analysis necessitate extensive training data for better generalization. However, obtaining sufficient diagnostic and surgical data is still challenging, mainly due to the inherent cost of data curation and the need of experts for data annotation. Moreover, increased privacy and legal compliance concerns can make data sharing across clinical sites or regions difficult. Another ubiquitous challenge the medical datasets face is inevitable domain shifts among the collected data at the different centers. To this end, we propose a Client-server deep federated architecture for cross-domain adaptation. A server hosts a set of immutable parameters common to both the source and target domains. The clients consist of the respective domain-specific parameters and make requests to the server while learning their parameters and inferencing. We evaluate our framework in two benchmark datasets, demonstrating applicability in computer-assisted interventions for endoscopic polyp segmentation and diagnostic skin lesion detection and analysis. Our extensive quantitative and qualitative experiments demonstrate the superiority of the proposed method compared to competitive baseline and state-of-the-art methods. Codes are available at: https://github.com/thetna/distributed-da