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- W4360993981 abstract "Real-world health data for machine learning tasks addresses many issues related to storage, access and privacy concerns on the centralized databases. Distributed data silos need to be secured and privacy assured for the access to multiple sites and reduce the risk of breach. A federated learning framework that learns global model from the distributed health data at different sites and provide secure access to emanate. The adaptive privacy mechanism on the sites is further a protective model to prevent potential attacks. In this paper a federated model with adaptive privacy is discussed. A simulation of the federated learning mechanism with federated sites with data silos and the differential privacy as adaptive privacy has been experimented. The results are satisfactory and found the variability of the differential privacy before and after the application of the privacy preservation." @default.
- W4360993981 created "2023-03-30" @default.
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- W4360993981 date "2023-01-01" @default.
- W4360993981 modified "2023-10-11" @default.
- W4360993981 title "Federated Learning and Adaptive Privacy Preserving in Healthcare" @default.
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- W4360993981 doi "https://doi.org/10.1007/978-3-031-27499-2_51" @default.
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