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This presentation by 真呼人金平 in the course ‘マルチモーダルバイオインフォマティックス特論’ focuses on the use of Federated Learning (FL) for brain tumor classification. It highlights the advantages of FL in preserving data privacy compared to centralized learning, tackles challenges like non-IID data, and examines techniques such as FedAvg and FedProx. Data analysis explores the classification of tumor types including glioma, meningioma, pituitary tumors, and no tumor, showcasing FL's impact on accuracy and comparing...