Journal of Scientific Engineering Advances

Abstract

STS are rare malignant disorders, and numerous histological subtypes exist that impede the diagnostic accuracy due to small datasets that are annotated by experts at institutions. The author presents a priva cy-conserving federated learning architecture in this paper that enables the collaborative. training on a model across multiple medical institutions without breaching patient privacy. Our is an algorithm, which integrates the privacy of differentials, the homomorphic encryption, and the secure multi-party. compu tation. The experiments of multi-institutional histopathology data of large scale confirm. that federated learning can equally achieve diagnostics, as centralized models. It eliminates the privacy threat and, (87.3% vs. 89.1% accuracy) besides, it is more precise. The framework addresses non-IID. data distri bution, communication efficiency, and resistance to attack vectors and demonstrates federal federated. learning to be a feasible solution to the diagnostics of rare cancer and satisfies the regulations at the same time.and governance enhancement