Deep learning-based techniques have been widely utilized for brain tumor segmentation using both single and multi-modal Magnetic Resonance Imaging (MRI) images. Most current studies focus on centralized training due to the intrinsic challenge of data sharing across clinics. To mitigate privacy concerns, researchers have introduced Federated Learning (FL) methods to brain tumor segmentation tasks. However, currently such methods are focusing on single modal MRI, with limited study on multi-modal MRI. The challenges include complex structure, large-scale parameters, and overfitting issues of the FL based methods using multi-modal MRI. To address the above challenges, we propose a novel multi-modal FL framework for brain tumor segmentation (Fed-MUnet) that is suitable for FL training. We evaluate our approach with the BraTS2022 datasets, which are publicly available. The experimental results demonstrate that our framework achieves FL nature of distributed learning and privacy preserving. For the enhancing tumor, tumor core and whole tumor, the mean of five major metrics were 87.5%, 90.6% and 92.2%, respectively, which were higher than SOTA methods while preserving privacy. In terms of parameters count, quantity of floating-point operations (FLOPs) and inference, Fed-MUnet is Pareto optimal compared with the state-of-the-art segmentation backbone while achieves higher performance and tackles privacy issue. Our codes are open-sourced at https://github.com/Arnold-Jun/Fed-MUnet.
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