Federated Learning for Private Cancer Diagnosis With Exascale Computing

Abstract

This study delves into the intersection of federated learning, privacy preservation, and exascale computing to advance the field of cancer diagnosis. Employing a federated learning framework, the research addresses the imperative need for collaborative, yet privacy-conscious, approaches to healthcare data analysis. Focusing on human cancer diagnosis and detection, the authors leverage the power of exascale computing to handle massive datasets distributed across diverse medical institutions. The proposed methodology ensures privacy by design, enabling secure model training without centralized data aggregation. The findings showcase the efficacy of federated learning and exascale computing in achieving accurate and timely cancer diagnoses while upholding patient privacy, thus paving the way for transformative advancements in personalized and secure healthcare analytics