mlsanityData to 7 Oct 2026

Robust Split Federated Learning for U-shaped Medical Image Networks

arXiv 2212.06378 ↗First posted 13 Dec 2022eess.IV cs.CV cs.DCcited by 11

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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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 28 of its 56 references that are in the map. First posted on arXiv 13 Dec 2022.

Builds on

28 of 56 references.

References · 28

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16 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 5; 2026: 1.

11 papers.

  1. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 125
  2. 2023Optimal Resource Allocation for U-Shaped Parallel Split Learningcited by 33
  3. 2025SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)cited by 7
  4. 2024The Impact of Cut Layer Selection in Split Federated Learningcited by 6
  5. 2024HierSFL: Local Differential Privacy-aided Split Federated Learning in Mobile Edge Computingcited by 3
  6. 2024SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustmentscited by 2
  7. 2025A Novel Approach to Breast Cancer Segmentation using U-Net Model with Attention Mechanisms and FedProxcited by 1
  8. 2025FedOptima: Optimizing Resource Utilization in Federated Learningcited by 0
  9. 2025NWaaS: A Non-Intrusive and Privacy-Preserving Watermarking-as-a-Service System with Adaptive Resource Schedulingcited by 0
  10. 2025ZORRO: Zero-Knowledge Robustness and Privacy for Split Learning (Full Version)cited by 0
  11. 2026USPLIT-VQA: U-Shaped Split Learning for Visual Question Answering with Contribution-Aware Weighted Aggregationcited by 0