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

• URL: http://arxiv.org/abs/2605.04108v1
• Date: Mon, 04 May 2026 16:43:22 GMT
• Title: MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation
• Abstract summary: This study proposes textbfMuCALD-SplitFed, a multi-task SplitFed framework that integrates causal representation learning and latent diffusion.<n> Experiments show MuCALD-SplitFed consistently improves segmentation, while baseline SplitFed fails to converge.<n>The proposed approach further reduces information leakage at split points, mitigating reconstruction-based and membership inference attacks.
• Score: 3.377298662011438
• License: http://creativecommons.org/licenses/by/4.0/
• 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: https://github.com/ChamaniS/MuCALD_SplitFed.

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