# Robust Split Federated Learning for U-shaped Medical Image Networks

arXiv: https://arxiv.org/abs/2212.06378 · HTML: https://mlsanity.com/paper/2212.06378/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 56 references that are in the map.
- First posted on arXiv 13 Dec 2022.

## Builds on (most cited first, 28 of 28)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space](https://mlsanity.com/paper/2103.06030/) (2021)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)](https://mlsanity.com/paper/1702.00288/) (2017)
- [U-Net and its variants for medical image segmentation: theory and applications](https://mlsanity.com/paper/2011.01118/) (2020)
- [CrypTen: Secure Multi-Party Computation Meets Machine Learning](https://mlsanity.com/paper/2109.00984/) (2021)
- [Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2106.06047/) (2021)
- [Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT](https://mlsanity.com/paper/1708.08333/) (2017)
- [Detailed comparison of communication efficiency of split learning and federated learning](https://mlsanity.com/paper/1909.09145/) (2019)
- [Split Learning for collaborative deep learning in healthcare](https://mlsanity.com/paper/1912.12115/) (2019)
- [DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising](https://mlsanity.com/paper/2108.10772/) (2021)
- [Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer](https://mlsanity.com/paper/2210.00912/) (2022)
- [Advancements of federated learning towards privacy preservation: from federated learning to split learning](https://mlsanity.com/paper/2011.14818/) (2020)
- [SplitAVG: A heterogeneity-aware federated deep learning method for medical imaging](https://mlsanity.com/paper/2107.02375/) (2021)
- [Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance](https://mlsanity.com/paper/2206.13079/) (2022)
- [Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learning](https://mlsanity.com/paper/2107.05917/) (2021)
- [Hercules: Boosting the Performance of Privacy-preserving Federated Learning](https://mlsanity.com/paper/2207.04620/) (2022)
- [Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation](https://mlsanity.com/paper/2208.10553/) (2022)

## Built on by (most cited first, 11 of 11)

- [Mobile Edge Intelligence for Large Language Models: A Contemporary Survey](https://mlsanity.com/paper/2407.18921/) (2024)
- [Optimal Resource Allocation for U-Shaped Parallel Split Learning](https://mlsanity.com/paper/2308.08896/) (2023)
- [SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)](https://mlsanity.com/paper/2501.06650/) (2025)
- [The Impact of Cut Layer Selection in Split Federated Learning](https://mlsanity.com/paper/2412.15536/) (2024)
- [HierSFL: Local Differential Privacy-aided Split Federated Learning in Mobile Edge Computing](https://mlsanity.com/paper/2401.08723/) (2024)
- [SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustments](https://mlsanity.com/paper/2405.04875/) (2024)
- [A Novel Approach to Breast Cancer Segmentation using U-Net Model with Attention Mechanisms and FedProx](https://mlsanity.com/paper/2510.19118/) (2025)
- [FedOptima: Optimizing Resource Utilization in Federated Learning](https://mlsanity.com/paper/2504.13850/) (2025)
- [NWaaS: A Non-Intrusive and Privacy-Preserving Watermarking-as-a-Service System with Adaptive Resource Scheduling](https://mlsanity.com/paper/2507.18036/) (2025)
- [ZORRO: Zero-Knowledge Robustness and Privacy for Split Learning (Full Version)](https://mlsanity.com/paper/2509.09787/) (2025)
- [USPLIT-VQA: U-Shaped Split Learning for Visual Question Answering with Contribution-Aware Weighted Aggregation](https://mlsanity.com/paper/2609.12168/) (2026)

## Abstract (arXiv)

U-shaped networks are widely used in various medical image tasks, such as segmentation, restoration and reconstruction, but most of them usually rely on centralized learning and thus ignore privacy issues. To address the privacy concerns, federated learning (FL) and split learning (SL) have attracted increasing attention. However, it is hard for both FL and SL to balance the local computational cost, model privacy and parallel training simultaneously. To achieve this goal, in this paper, we propose Robust Split Federated Learning (RoS-FL) for U-shaped medical image networks, which is a novel hybrid learning paradigm of FL and SL. Previous works cannot preserve the data privacy, including the input, model parameters, label and output simultaneously. To effectively deal with all of them, we design a novel splitting method for U-shaped medical image networks, which splits the network into three parts hosted by different parties. Besides, the distributed learning methods usually suffer from a drift between local and global models caused by data heterogeneity. Based on this consideration, we propose a dynamic weight correction strategy (DWCS) to stabilize the training process and avoid model drift. Specifically, a weight correction loss is designed to quantify the drift between the models from two adjacent communication rounds. By minimizing this loss, a correction model is obtained. Then we treat the weighted sum of correction model and final round models as the result. The effectiveness of the proposed RoS-FL is supported by extensive experimental results on different tasks. Related codes will be released at https://github.com/Zi-YuanYang/RoS-FL.