[2605.04108] MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.04108 (cs)
[Submitted on 4 May 2026]
Title:MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation
Authors:Chamani Shiranthika, Hadi Hadizadeh, Parvaneh Saeedi
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Abstract: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 \textbf{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: this https URL.
Comments: Accepted to oral presentation and conference proceedings at IEEE International Conference on Image Processing (ICIP 2026), Finland
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.04108 [cs.CV]
(or arXiv:2605.04108v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.04108
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Chamani Shiranthika Jayakody Kankanamalage [view email] [v1] Mon, 4 May 2026 16:43:22 UTC (4,252 KB)
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