Isbn: 9780443138973 - Federated Learning for Digital Healthcare Systems (intelligent Data-centric Systems) (9 results)

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  • Language: English

    Published by Academic Press, 2024

    0443138974 / 9780443138973

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  • Language: English

    Published by Academic Press, 2024

    0443138974 / 9780443138973

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    Condition: New. In English.

  • Language: English

    Published by Academic Press, 2024

    0443138974 / 9780443138973

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  • Language: English

    Published by Academic Press, 2024

    0443138974 / 9780443138973

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    Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK

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  • Language: English

    Published by Academic Press, 2024

    0443138974 / 9780443138973

    • Softcover

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  • Language: English

    Published by Elsevier Science Publishing Co Inc, US, 2024

    0443138974 / 9780443138973

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    Paperback. Condition: New. Federated Learning for Digital Healthcare Systems critically examines the key factors that contribute to the problem of applying machine learning in healthcare systems and investigates how federated learning can be employed to address the problem. The book discusses, examines, and compares the applications of federated learning solutions in emerging digital healthcare systems, providing a critical look in terms of the required resources, computational complexity, and system performance.In the first section, chapters examine how to address critical security and privacy concerns and how to revamp existing machine learning models. In subsequent chapters, the book's authors review recent advances to tackle emerging efficient and lightweight algorithms and protocols to reduce computational overheads and communication costs in wireless healthcare systems. Consideration is also given to government and economic regulations as well as legal considerations when federated learning is applied to digital healthcare systems. …

  • Language: English

    Published by Elsevier Science, 2024

    0443138974 / 9780443138973

    • Softcover

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    Condition: New. Provides insights into real-world scenarios of the design, development, deployment, application, management, and benefits of federated learning in emerging digital healthcare systemsHighlights the need to design efficient federated learning.

  • Language: English

    Published by Elsevier Science Publishing Co Inc, US, 2024

    0443138974 / 9780443138973

    • Softcover

    Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK

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    Paperback. Condition: New. Federated Learning for Digital Healthcare Systems critically examines the key factors that contribute to the problem of applying machine learning in healthcare systems and investigates how federated learning can be employed to address the problem. The book discusses, examines, and compares the applications of federated learning solutions in emerging digital healthcare systems, providing a critical look in terms of the required resources, computational complexity, and system performance.In the first section, chapters examine how to address critical security and privacy concerns and how to revamp existing machine learning models. In subsequent chapters, the book's authors review recent advances to tackle emerging efficient and lightweight algorithms and protocols to reduce computational overheads and communication costs in wireless healthcare systems. Consideration is also given to government and economic regulations as well as legal considerations when federated learning is applied to digital healthcare systems. …

  • Language: English

    Published by Elsevier Inc Jun 2024, 2024

    0443138974 / 9780443138973

    • Softcover

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    Taschenbuch. Condition: Neu. Neuware - Federated Learning for Digital Healthcare Systems critically examines the key factors that contribute to the problem of applying machine learning in healthcare systems and investigates how federated learning can be employed to address the problem. The book discusses, examines, and compares the applications of federated learning solutions in emerging digital healthcare systems, providing a critical look in terms of the required resources, computational complexity, and system performance.In the first section, chapters examine how to address critical security and privacy concerns and how to revamp existing machine learning models. In subsequent chapters, the book's authors review recent advances to tackle emerging efficient and lightweight algorithms and protocols to reduce computational overheads and communication costs in wireless healthcare systems. Consideration is also given to government and economic regulations as well as legal considerations when federated learning is applied to digital healthcare systems.…