OPTIMIZING FEDERATED SHUFFLING WITH PRIVACY FOR BRAIN TUMOR MRI CLASSIFICATION

The increasing use of medical imaging for diagnosing brain tumors has highlighted the need for effective and privacy-preserving machine learning models. This study presents a solution for brain tumor classification from MRI scans using federated learning, which ensures patient data privacy by keeping the data decentralized and only sharing model updates. A deep learning approach, utilizing transfer learning with the VGG-16 architecture, is employed to classify images into different tumor types: pituitary, meningioma, glioma, and no tumor. Data augmentation techniques are applied to enhance the training dataset, addressing class imbalance issues. The model is trained in a federated learning environment with multiple clients, where each client trains the model on their local dataset, and the results are aggregated to update the global model. After training, the model's performance is evaluated using metrics such as classification accuracy, precision, recall, F1-score, and a confusion matrix. This approach allows for high accuracy in tumor classification while ensuring data privacy through federated learning. The proposed method aims to facilitate efficient, privacy-preserving medical image classification in a distributed setting, particularly in scenarios with sensitive data.