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Handbook on Federated Learning (Paperback)
Published by Taylor & Francis Ltd, London, 2025
ISBN 10: 1032471638 ISBN 13: 9781032471631
Language: English
Paperback. Condition: new. Paperback. Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects. Federated learning is a Distributed Machine Learning model that has been used in many applications today. Most edge devices can execute models with local dataset since their computation power is unutilized. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Condition: New.
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£ 58.95
Convert currency£ 6.50 shipping from United Kingdom to U.S.A.Quantity: 3 available
Add to basketCondition: New.
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Condition: New. 1st edition NO-PA16APR2015-KAP.
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Condition: New.
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Handbook on Federated Learning (Paperback)
Published by Taylor & Francis Ltd, London, 2025
ISBN 10: 1032471638 ISBN 13: 9781032471631
Language: English
£ 54.29
Convert currency£ 27.76 shipping from Australia to U.S.A.Quantity: 1 available
Add to basketPaperback. Condition: new. Paperback. Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects. Federated learning is a Distributed Machine Learning model that has been used in many applications today. Most edge devices can execute models with local dataset since their computation power is unutilized. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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£ 80.81
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Add to basketPaperback. Condition: Brand New. 362 pages. 9.18x6.12x9.21 inches. In Stock.
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Handbook on Federated Learning (Paperback)
Published by Taylor & Francis Ltd, London, 2025
ISBN 10: 1032471638 ISBN 13: 9781032471631
Language: English
£ 66.99
Convert currency£ 37 shipping from United Kingdom to U.S.A.Quantity: 1 available
Add to basketPaperback. Condition: new. Paperback. Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects. Federated learning is a Distributed Machine Learning model that has been used in many applications today. Most edge devices can execute models with local dataset since their computation power is unutilized. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Condition: New.
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Condition: New. 1st edition NO-PA16APR2015-KAP.
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£ 153.86
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Handbook on Federated Learning: Advances, Applications and Opportunities
Published by Taylor & Francis Ltd, 2023
ISBN 10: 103247162X ISBN 13: 9781032471624
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
£ 143.92
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Add to basketHardback. Condition: New. New copy - Usually dispatched within 4 working days. 830.
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Handbook on Federated Learning: Advances, Applications and Opportunities
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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£ 156.02
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Condition: As New. Unread book in perfect condition.
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Handbook on Federated Learning : Advances, Applications and Opportunities
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£ 154.97
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£ 159
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Add to basketpaperback. Condition: New. New. Ships from Multiple Locations. book.
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Handbook on Federated Learning : Advances, Applications and Opportunities
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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£ 174.11
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Add to basketCondition: As New. Unread book in perfect condition.
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£ 210.52
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Add to basketHardcover. Condition: Brand New. 362 pages. 9.19x6.13x0.81 inches. In Stock.
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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Handbook on Federated Learning
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
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£ 63.39
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Add to basketPAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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£ 100.29
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Add to basketTaschenbuch. Condition: Neu. Handbook on Federated Learning | Advances, Applications and Opportunities | Saravanan Krishnan (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2025 | CRC Press | EAN 9781032471631 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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HRD. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Handbook on Federated Learning
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
£ 163.33
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Add to basketHRD. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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£ 182.19
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Saravanan Krishnan is working as Associate Professor at the Department of Computer Science & Engineering, College of Engineering, Guindy, Anna University, Tirunelveli, India. He has published papers in 14 international conferences and 30 reput.
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£ 188.74
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Add to basketBuch. Condition: Neu. Handbook on Federated Learning | Advances, Applications and Opportunities | Saravanan Krishnan (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2023 | CRC Press | EAN 9781032471624 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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£ 225.72
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Add to basketBuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Federated learning is a Distributed Machine Learning model that has been used in many applications today. Most edge devices can execute models with local dataset since their computation power is unutilized.
