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

Abstract (excerpt). 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 …

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Recommended citation: Thu Thuy Le, Nhat Truong Pham, Phuong-Nam Tran, Duc Ngoc Minh Dang. (2024). "Federated Learning with U-Net for Brain Tumor Segmentation: Impact of Client Numbers and Data Distribution." 2024 15th International Conference on Information and Communication Technology Convergence (ICTC), 2048-2053.
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