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Split learning (SL) has been proposed to train deep learning models in adecentralized manner. For decentralized healthcare applications with verticaldata partitioning, SL can be beneficial as it allows institutes withcomplementary features or images for a shared set of patients to jointlydevelop more robust and generalizable models. In this work, we propose"Split-U-Net" and successfully apply SL for collaborative biomedical imagesegmentation. Nonetheless, SL requires the exchanging of intermediateactivation maps and gradients to allow training models across different featurespaces, which might leak data and raise privacy concerns. Therefore, we alsoquantify the amount of data leakage in common SL scenarios for biomedical imagesegmentation and provide ways to counteract such leakage by applyingappropriate defense strategies.

Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation