The healthcare industry has found it challenging to build a powerful global classification model due to the scarcity and diversity of medical data. The leading cause is privacy, which restricts data sharing among healthcare providers. Federated learning (FL) can contribute to developing classification models by protecting data privacy. This study has tested various federated techniques in a peer-to-peer setting to classify brain Magnetic Resonance Images (MRI). The authors propose various aggregation strategies for FL, including Federated Averaging (FedAvg), Quantum FL with FedAVG (QFedAvg) and Fault Tolerant FedAvg (Ft-FedAvg) and FedAvg with differential privacy (Dp-FedAvg). In each approach, a custom Convolutional Neural Network (CNN) model is applied to compute locally run nodes with different parts of the same brain MRI dataset for 10, 20 and 30 training and test rounds. A central server and CNN-based three federated clients are included in the FL-based brain tumour classification model to exchange data and combine the model weights on the server, which are sent from local devices to the server. The superiority of the performance of the proposed model is demonstrated by comparing it with traditional methods on various performance metrics. Experimental results show that in brain MRI dataset classification using FL approaches, FedAVg showed the best performance with 85.55% and 84.60% success for 10 and 20 rounds, respectively, while Ft-FedAvg showed the best performance with 85.80% success for 30 rounds for test set. Statistical results obtained from FL approaches showed that FedAvg and Ft-FedAvg have superior performance with regard to accuracy and robustness in comparison with the others.

中文翻译：

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使用基于隐私保护联邦学习的卷积神经网络对磁共振图像进行脑肿瘤分类

由于医疗数据的稀缺性和多样性，医疗保健行业发现建立强大的全球分类模型具有挑战性。主要原因是隐私，它限制了医疗保健提供者之间的数据共享。联邦学习（FL）可以通过保护数据隐私来促进分类模型的开发。这项研究在点对点环境中测试了各种联合技术，以对脑部磁共振图像 (MRI) 进行分类。作者提出了各种 FL 聚合策略，包括联邦平均 (FedAvg)、带有 FedAVG 的量子 FL (QFedAvg) 和容错 FedAvg (Ft-FedAvg) 以及具有差分隐私的 FedAvg (Dp-FedAvg)。在每种方法中，都应用定制的卷积神经网络 (CNN) 模型来计算本地运行的节点，其中包含同一大脑 MRI 数据集的不同部分，进行 10、20 和 30 轮训练和测试。基于 FL 的脑肿瘤分类模型中包含中央服务器和基于 CNN 的三个联合客户端，用于交换数据并组合服务器上的模型权重，这些权重从本地设备发送到服务器。通过在各种性能指标上与传统方法进行比较，证明了所提出模型的性能优越性。实验结果表明，在使用 FL 方法的脑 MRI 数据集分类中，FedAVg 表现出最好的性能，在 10 轮和 20 轮测试中分别达到 85.55% 和 84.60% 的成功率，而 Ft-FedAvg 在 30 轮测试中表现出最好的性能，达到 85.80% 的成功率放。FL方法获得的统计结果表明，与其他方法相比，FedAvg和Ft-FedAvg在准确性和鲁棒性方面具有优越的性能。