Federated Learning with U-Net for Brain Tumor Segmentation: Impact of Client Numbers and Data Distribution

Brain tumor segmentation plays a crucial role in diagnosis and treatment planning. However, sharing patient data for training deep learning models raises privacy concerns. This paper proposes a federated learning (FL) approach utilizing a U-Net architecture for the segmentation of brain tumors. We evaluate the performance of federated U-Net models under various data distribution and varying numbers of clients. Specifically, we compare the effectiveness of two FL methods: Federated Averaging (FedAvg) and Federated Stochastic Gradient Descent (FedSGD). Through experiments conducted on the BraTS dataset, we observe that as the number of clients increases, the overall performance of the models tends to decrease. Moreover, we find that skewed data distribution often outperforms equal data division. Additionally, we consistently observe that FedAvg yields superior results compared to FedSGD. Our proposed approach enables hospitals to train models on local data collaboratively without directly sharing sensitive information. This preserves patient privacy while ensuring accurate tumor segmentation. The results of our study underscore the significance of strategic data distribution in FL environments and provide valuable insights for optimizing FL strategies in medical imaging applications.