mlsanityData to 14 Oct 2026

MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation

arXiv 2605.04108 ↗First posted 4 May 2026cs.CVcited by 0

On the map: Diffusion models → Robustness, privacy and adversarial aspects

Abstract (arXiv)

Federated Learning enables decentralized training by aggregating model updates across clients without sharing raw data, while Split Federated Learning further partitions the model between clients and a server to reduce computation and communication at the client side. However, decentralized medical institutions rarely operate on a single shared task, making standard Federated and SplitFed collaborations poorly aligned with real clinical workflows. Multi-task FL extends these frameworks by allowing clients to handle different tasks, but often introduces instability and privacy vulnerabilities. This study proposes MuCALD-SplitFed, a multi-task SplitFed framework that integrates causal representation learning and latent diffusion. Experiments show MuCALD-SplitFed consistently improves segmentation, while baseline SplitFed fails to converge. The proposed approach further reduces information leakage at split points, mitigating reconstruction-based and membership inference attacks. Additionally, MuCALD SplitFed outperforms state-of-the-art personalized FL and multi-task FL approaches. The code repository is: https://github.com/ChamaniS/MuCALDSplitFed.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models. Builds on 24 of its 36 references that are in the map. First posted on arXiv 4 May 2026.

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24 of 36 references.

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  1. 2024CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense30 Oct

Other references · 23

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,917
  5. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  6. 2017Federated Multi-Task Learningcited by 672
  7. 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 620
  8. 2019Kvasir-SEG: A Segmented Polyp Datasetcited by 589
  9. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  10. 2019Federated Learning with Personalization Layerscited by 541
  11. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  12. 2020UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentationcited by 332
11 more
  1. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  2. 2018Distributed learning of deep neural network over multiple agentscited by 279
  3. 2019Learning Sparse Nonparametric DAGscited by 207
  4. 2019Causality matters in medical imagingcited by 193
  5. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 192
  6. 2022Diffusion Causal Models for Counterfactual Estimationcited by 87
  7. 2024Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Modelscited by 18
  8. 2023FedBone: Towards Large-Scale Federated Multi-Task Learningcited by 14
  9. 2025Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task Learning: A Sheaf-Theoretic Approachcited by 10
  10. 2025FedAlign: Federated Domain Generalization with Cross-Client Feature Alignmentcited by 5
  11. 2024MACAW: A Causal Generative Model for Medical Imagingcited by 3

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