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Añadir al carritoHardback. Condición: New. New copy - Usually dispatched within 4 working days.
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Añadir al carritoHardcover. Condición: Brand New. 344 pages. 9.18x6.12x9.21 inches. In Stock.
Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2024
ISBN 10: 103277164X ISBN 13: 9781032771649
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Añadir al carritoHardcover. Condición: new. Hardcover. Federated machine learning is a novel approach to combining distributed machine learning, cryptography, security, and incentive mechanism design. It allows organizations to keep sensitive and private data on users or customers decentralized and secure, helping them comply with stringent data protection regulations like GDPR and CCPA.Artificial Intelligence Using Federated Learning: Fundamentals, Challenges, and Applications enables training AI models on a large number of decentralized devices or servers, making it a scalable and efficient solution. It also allows organizations to create more versatile AI models by training them on data from diverse sources or domains. This approach can unlock innovative use cases in fields like healthcare, finance, and IoT, where data privacy is paramount.The book is designed for researchers working in Intelligent Federated Learning and its related applications, as well as technology development, and is also of interest to academicians, data scientists, industrial professionals, researchers, and students. Federated machine learning is a novel approach to combining distributed machine learning, cryptography, security, and incentive mechanism design. It allows organizations to keep sensitive and private data on users or customers decentralized and secure, helping them comply with stringent data protection regulations like GDPR and CCPA. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2024
ISBN 10: 103277164X ISBN 13: 9781032771649
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EUR 209,56
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Añadir al carritoHardcover. Condición: new. Hardcover. Federated machine learning is a novel approach to combining distributed machine learning, cryptography, security, and incentive mechanism design. It allows organizations to keep sensitive and private data on users or customers decentralized and secure, helping them comply with stringent data protection regulations like GDPR and CCPA.Artificial Intelligence Using Federated Learning: Fundamentals, Challenges, and Applications enables training AI models on a large number of decentralized devices or servers, making it a scalable and efficient solution. It also allows organizations to create more versatile AI models by training them on data from diverse sources or domains. This approach can unlock innovative use cases in fields like healthcare, finance, and IoT, where data privacy is paramount.The book is designed for researchers working in Intelligent Federated Learning and its related applications, as well as technology development, and is also of interest to academicians, data scientists, industrial professionals, researchers, and students. Federated machine learning is a novel approach to combining distributed machine learning, cryptography, security, and incentive mechanism design. It allows organizations to keep sensitive and private data on users or customers decentralized and secure, helping them comply with stringent data protection regulations like GDPR and CCPA. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Librería: preigu, Osnabrück, Alemania
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Añadir al carritoBuch. Condición: Neu. Artificial Intelligence Using Federated Learning | Fundamentals, Challenges, and Applications | Ahmed A Elngar (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2024 | CRC Press | EAN 9781032771649 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 239,60
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Añadir al carritoBuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Federated machine learning is a novel approach to combining distributed machine learning, cryptography, security, and incentive mechanism design. It allows organizations to keep sensitive and private data on users or customers decentralized and secure, helping them comply with stringent data protection regulations like GDPR and CCPA.
Idioma: Inglés
Publicado por Taylor & Francis Ltd, London, 2024
ISBN 10: 103277164X ISBN 13: 9781032771649
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 281,05
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Añadir al carritoHardcover. Condición: new. Hardcover. Federated machine learning is a novel approach to combining distributed machine learning, cryptography, security, and incentive mechanism design. It allows organizations to keep sensitive and private data on users or customers decentralized and secure, helping them comply with stringent data protection regulations like GDPR and CCPA.Artificial Intelligence Using Federated Learning: Fundamentals, Challenges, and Applications enables training AI models on a large number of decentralized devices or servers, making it a scalable and efficient solution. It also allows organizations to create more versatile AI models by training them on data from diverse sources or domains. This approach can unlock innovative use cases in fields like healthcare, finance, and IoT, where data privacy is paramount.The book is designed for researchers working in Intelligent Federated Learning and its related applications, as well as technology development, and is also of interest to academicians, data scientists, industrial professionals, researchers, and students. Federated machine learning is a novel approach to combining distributed machine learning, cryptography, security, and incentive mechanism design. It allows organizations to keep sensitive and private data on users or customers decentralized and secure, helping them comply with stringent data protection regulations like GDPR and CCPA. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.