Medical institutions often revoke data access due to the privacy concern of patients. Federated Learning (FL) is a collaborative learning paradigm that can generate an unbiased global model based on collecting updates from local models trained by client’s data while keeping the local data private. This study aims to address the centralized data collection issue through the application of FL on brain tumor identification from MRI images. At first, several CNN models were trained using the MRI data and the best three performing CNN models were selected to form different variants of ensemble classifiers. Afterward, the FL model was constructed using the ensemble architecture. It was trained using model weights from the local model without sharing the client’s data (MRI images) using the FL approach. Experimental results show only a slight decline in the performance of the FL approach as it achieved 91.05% accuracy compared to the 96.68% accuracy of the base ensemble model. Additionally, same approach was taken for another slightly larger dataset to prove the scalability of the method. This study shows that the FL approach can achieve privacy-protected tumor classification from MRI images without compromising much accuracy compared to the traditional deep learning approach.

出于对患者隐私的担忧，医疗机构经常撤销数据访问权限。联邦学习 (FL) 是一种协作学习范例，可以根据从客户数据训练的本地模型收集更新来生成无偏差的全局模型，同时保持本地数据的私密性。本研究旨在通过应用 FL 从 MRI 图像识别脑肿瘤来解决集中数据收集问题。首先，使用 MRI 数据训练几个 CNN 模型，并选择性能最好的三个 CNN 模型来形成集成分类器的不同变体。随后，使用集成架构构建了 FL 模型。它使用本地模型的模型权重进行训练，而无需使用 FL 方法共享客户的数据（MRI 图像）。实验结果表明，FL 方法的性能仅略有下降，与基本集成模型 96.68% 的准确率相比，它实现了 91.05% 的准确率。此外，对另一个稍大的数据集采取了相同的方法，以证明该方法的可扩展性。这项研究表明，与传统的深度学习方法相比，FL 方法可以从 MRI 图像中实现隐私保护的肿瘤分类，而不会影响太多的准确性。