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arXiv Computer Vision By Chamani Shiranthika, Hadi Hadizadeh, Parvaneh Saeedi

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

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MuCALD‑SplitFed is a multi‑task split‑federated learning framework that combines causal representation learning with latent diffusion to enable privacy‑preserving medical image segmentation across decentralized institutions. It addresses the instability and privacy issues of existing multi‑task federated approaches by reducing information leakage at split points and improving convergence. Experiments demonstrate that MuCALD‑SplitFed outperforms baseline split‑federated, personalized federated, and multi‑task federated methods on segmentation tasks.

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