arXiv:2611.09954v1 Announce Type: cross
Abstract: Federated Learning (FL), Split Learning (SL), and Split Federated Learning (SplitFed) enable collaborative medical image analysis without sharing raw patient data. However, intermediate features and gradients exchanged during SplitFed training may reveal sensitive information through reconstruction attacks. To address this vulnerability, we propose \textbf{DiffusedSplitFed}, a SplitFed framework that integrates Latent Denoising Diffusion Models (LDDMs) at client–server split points to obfuscate transmitted representations while preserving segmentation performance. Three architectural variants (V1–V3) are developed using different combinations of dual conditioning and global feature fusion to investigate trade-offs among reconstruction resistance, segmentation accuracy, and computational complexity. Experiments on three medical imaging datasets (HAM10K, Blastocysts, and FHPsAOPMSB), supported by multiple independent runs and paired statistical analyses, demonstrate improved resistance to the evaluated image reconstruction attacks compared with Baseline SplitFed, while maintaining competitive segmentation performance. Comparisons with existing diffusion-based segmentation methods further demonstrate competitive segmentation accuracy under the evaluated settings. A conditional convergence analysis is also provided to characterize the effects of LDDM-induced gradient perturbations and local optimization on training. These findings highlight the potential of latent diffusion-based representation obfuscation for reconstruction-resilient collaborative medical image segmentation. The implementation is available at https://github.com/ChamaniS/DiffusedSplitFed.
Abstract: Federated Learning (FL), Split Learning (SL), and Split Federated Learning (SplitFed) enable collaborative medical image analysis without sharing raw patient data. However, intermediate features and gradients exchanged during SplitFed training may reveal sensitive information through reconstruction attacks. To address this vulnerability, we propose \textbf{DiffusedSplitFed}, a SplitFed framework that integrates Latent Denoising Diffusion Models (LDDMs) at client–server split points to obfuscate transmitted representations while preserving segmentation performance. Three architectural variants (V1–V3) are developed using different combinations of dual conditioning and global feature fusion to investigate trade-offs among reconstruction resistance, segmentation accuracy, and computational complexity. Experiments on three medical imaging datasets (HAM10K, Blastocysts, and FHPsAOPMSB), supported by multiple independent runs and paired statistical analyses, demonstrate improved resistance to the evaluated image reconstruction attacks compared with Baseline SplitFed, while maintaining competitive segmentation performance. Comparisons with existing diffusion-based segmentation methods further demonstrate competitive segmentation accuracy under the evaluated settings. A conditional convergence analysis is also provided to characterize the effects of LDDM-induced gradient perturbations and local optimization on training. These findings highlight the potential of latent diffusion-based representation obfuscation for reconstruction-resilient collaborative medical image segmentation. The implementation is available at https://github.com/ChamaniS/DiffusedSplitFed.
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