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Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.
Da: preigu, Osnabrück, Germania
EUR 157,95
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Federated Learning | A Comprehensive Overview of Methods and Applications | Heiko Ludwig (u. a.) | Taschenbuch | vi | Englisch | 2023 | Springer | EAN 9783030968984 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. pp. 544.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 181,89
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Federated Learning: A Comprehensive Overview of Methods and Applications presents an in-depth discussion of the most important issues and approaches to federated learning for researchers and practitioners.Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. Data are retained by data parties that participate in the FL process and are not shared with any other entity. This makes FL an increasingly popular solution for machine learning tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory or practical reasons.This book explains recent progress in research and the state-of-the-art development of Federated Learning (FL), from the initial conception of the field to first applications and commercial use. To obtain this broad and deep overview, leading researchers address the different perspectives of federated learning: the core machine learning perspective, privacy and security, distributed systems, and specific application domains. Readers learn about the challenges faced in each of these areas, how they are interconnected, and how they are solved by state-of-the-art methods.Following an overview on federated learning basics in the introduction, over the following 24 chapters, the reader will dive deeply into various topics. A first part addresses algorithmic questions of solving different machine learning tasks in a federated way, how to train efficiently, at scale, and fairly. Another part focuses on providing clarity on how to select privacy and security solutions in a way that can be tailored to specific use cases, while yet another considers the pragmatics of the systems where the federated learning process will run. The book also covers other important use cases for federated learning such as split learning and vertical federated learning. Finally, the book includes some chapters focusing on applying FL in real-world enterprise settings.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 181,89
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Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Federated Learning: A Comprehensive Overview of Methods and Applications presents an in-depth discussion of the most important issues and approaches to federated learning for researchers and practitioners.Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. Data are retained by data parties that participate in the FL process and are not shared with any other entity. This makes FL an increasingly popular solution for machine learning tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory or practical reasons.This book explains recent progress in research and the state-of-the-art development of Federated Learning (FL), from the initial conception of the field to first applications and commercial use. To obtain this broad and deep overview, leading researchers address the different perspectives of federated learning: the core machine learning perspective, privacy and security, distributed systems, and specific application domains. Readers learn about the challenges faced in each of these areas, how they are interconnected, and how they are solved by state-of-the-art methods.Following an overview on federated learning basics in the introduction, over the following 24 chapters, the reader will dive deeply into various topics. A first part addresses algorithmic questions of solving different machine learning tasks in a federated way, how to train efficiently, at scale, and fairly. Another part focuses on providing clarity on how to select privacy and security solutions in a way that can be tailored to specific use cases, while yet another considers the pragmatics of the systems where the federated learning process will run. The book also covers other important use cases for federated learning such as split learning and vertical federated learning. Finally, the book includes some chapters focusing on applying FL in real-world enterprise settings.
Lingua: Inglese
Editore: Springer-Nature New York Inc, 2023
ISBN 10: 3030968987 ISBN 13: 9783030968984
Da: Revaluation Books, Exeter, Regno Unito
EUR 276,89
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Aggiungi al carrelloPaperback. Condizione: Brand New. com edition. 540 pages. 9.25x6.10x1.10 inches. In Stock.
Lingua: Inglese
Editore: Springer-Nature New York Inc, 2022
ISBN 10: 3030968952 ISBN 13: 9783030968953
Da: Revaluation Books, Exeter, Regno Unito
EUR 278,93
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Aggiungi al carrelloHardcover. Condizione: Brand New. 540 pages. 9.25x6.10x1.46 inches. In Stock.
Lingua: Inglese
Editore: Springer-Nature New York Inc, 2022
ISBN 10: 3030968952 ISBN 13: 9783030968953
Da: Revaluation Books, Exeter, Regno Unito
EUR 166,77
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Aggiungi al carrelloHardcover. Condizione: Brand New. 540 pages. 9.25x6.10x1.46 inches. In Stock. This item is printed on demand.
Da: Majestic Books, Hounslow, Regno Unito
EUR 229,75
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. Print on Demand pp. 544.
Da: Majestic Books, Hounslow, Regno Unito
EUR 230,51
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 231,50
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp. 544.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 255,12
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