BrainDiffU-Net: A Distributed Diffusion-Based U-Net for Brain Tumor Segmentation

Precise segmentation of brain tumors is essential in medical imaging, supporting early diagnosis, personalized treatment planning, and effective monitoring of tumor development and response to therapies. While centralized deep learning models like U-Net are highly effective for this task, they raise substantial privacy concerns when healthcare or-ganizations need to exchange confidential patient information. To address these concerns, the BrainDiffU-Net framework is introduced, a diffusion-based U-Net model designed for decen-tralized learning environments. In this framework, each medical institution independently trains a model on its local dataset and exchanges model updates with neighboring institutions, ensuring data privacy without requiring a central server. The diffusion process allows model parameters to be shared securely across the network, facilitating collaborative learning across multiple institutions while preserving privacy. Simulations conducted on the Low-Grade Glioma (LGG) MRI Segmentation Dataset demonstrate that BrainDiffU-Net achieves segmentation performance comparable to centralized models and significantly outperforms non-cooperative strategies in terms of average loss and Dice coefficient. These results show that BrainDiffU-Net provides an optimal balance between segmentation accuracy and privacy preservation, making it a viable solution for decen-tralized medical applications. The source code is available at https://github.com/Parth-nXp/BrainDiffU-Net.