Bridging AI and Privacy: Federated Learning for Leukemia Diagnosis

Abstract

Leukemia is a heterogeneous group of hematologic malignancies, with acute lymphoblastic leukemia (ALL) being one of the most harmful forms. Accurate and early diagnosis is crucial for effective treatment, potentially saving lives. Recent advances in machine learning (ML) and deep learning (DL) have significantly enhanced diagnostic capabilities. However, these advancements often compromise the confidentiality of sensitive medical data. In this paper, we propose a federated learning (FL) framework for the binary classification of ALL versus normal cases. This framework leverages decentralized data from multiple clients, where each client trains its model locally on its own data, transmitting only model updates to a central server. The central server then aggregates these updates using the FedAvg algorithm, creating a global model while ensuring that patient data remains at its source, thereby preserving confidentiality. Using an EfficientNetV2S-based model architecture and a dataset of 10,661 images containing normal cells and lymphoblasts, our experiments demonstrate that the proposed FL approach achieves an accuracy of 95.6% and a kappa coefficient of 0.89. This performance is competitive with centralized methods while maintaining data privacy. These results highlight the potential of FL to revolutionize the clinical detection of acute lymphoblastic leukemia, offering a scalable and privacy-preserving solution for medical applications.

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