Federated Learning

Alexander Jung

ISBN 10: 9819510082 ISBN 13: 9789819510085

Edit� par Springer, 2026

Neuf(s) Couverture rigide

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New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. N� de r�f. du vendeur ABEOCT25-360933

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Synopsis :

Designed for students, engineers, and researchers, this book offers a principled yet practical roadmap to building secure, scalable, and trustworthy FL systems from scratch.

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

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Titre : Federated Learning
�diteur : Springer
Date d'�dition : 2026
Reliure : Couverture rigide
Etat : Brand New

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Federated Learning (eng)

Jung, Alexander

Edit� par Springer, 2026

ISBN 10 : 9819510082 ISBN 13 : 9789819510085

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Federated Learning | From Theory to Practice

Alexander Jung

Edit� par Springer, 2026

ISBN 10 : 9819510082 ISBN 13 : 9789819510085

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Federated Learning

Alexander Jung

Edit� par Springer-Verlag Gmbh Jan 2026, 2026

ISBN 10 : 9819510082 ISBN 13 : 9789819510085

Neuf Couverture rigide

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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -How can we train powerful machine learning models together across smartphones, hospitals, or financial institutions without ever sharing raw data This book delivers a compelling answer through the lens of federated learning (FL), a cutting-edge paradigm for decentralized, privacy-preserving machine learning. Designed for students, engineers, and researchers, this book offers a principled yet practical roadmap to building secure, scalable, and trustworthy FL systems from scratch.At the heart of this book is a unifying framework that treats FL as a network-regularized optimization problem. This elegant formulation allows readers to seamlessly address personalization, robustness, and fairness challenges often tackled in isolation. You ll learn how to structure FL networks based on task similarity, leverage graph-based methods and apply distributed optimization techniques to implement FL systems. Detailed pseudocode, intuitive explanations, and implementation-ready algorithms ensure you not only understand the theory but can apply it in real-world systems.Topics such as privacy leakage analysis, model heterogeneity, and adversarial resilience are treated with both mathematical rigor and accessibility. Whether you're building decentralized AI for regulated industries or in settings where data, users, or system conditions change over time, this book equips you to design FL systems that are both performant and trustworthy. 213 pp. Englisch. N� de r�f. du vendeur 9789819510085

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Federated Learning

Alexander Jung

Edit� par Springer, Springer Jan 2026, 2026

ISBN 10 : 9819510082 ISBN 13 : 9789819510085

Neuf Couverture rigide

impression � la demande

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -How can we train powerful machine learning models togetheracross smartphones, hospitals, or financial institutionswithout ever sharing raw data This book delivers a compelling answer through the lens of federated learning (FL), a cutting-edge paradigm for decentralized, privacy-preserving machine learning. Designed for students, engineers, and researchers, this book offers a principled yet practical roadmap to building secure, scalable, and trustworthy FL systems from scratch.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 232 pp. Englisch. N� de r�f. du vendeur 9789819510085

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Federated Learning : From Theory to Practice

Alexander Jung

Edit� par Springer, Springer, 2026

ISBN 10 : 9819510082 ISBN 13 : 9789819510085

Neuf Couverture rigide

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - How can we train powerful machine learning models together across smartphones, hospitals, or financial institutions without ever sharing raw data This book delivers a compelling answer through the lens of federated learning (FL), a cutting-edge paradigm for decentralized, privacy-preserving machine learning. Designed for students, engineers, and researchers, this book offers a principled yet practical roadmap to building secure, scalable, and trustworthy FL systems from scratch.At the heart of this book is a unifying framework that treats FL as a network-regularized optimization problem. This elegant formulation allows readers to seamlessly address personalization, robustness, and fairness challenges often tackled in isolation. You ll learn how to structure FL networks based on task similarity, leverage graph-based methods and apply distributed optimization techniques to implement FL systems. Detailed pseudocode, intuitive explanations, and implementation-ready algorithms ensure you not only understand the theory but can apply it in real-world systems.Topics such as privacy leakage analysis, model heterogeneity, and adversarial resilience are treated with both mathematical rigor and accessibility. Whether you're building decentralized AI for regulated industries or in settings where data, users, or system conditions change over time, this book equips you to design FL systems that are both performant and trustworthy. N� de r�f. du vendeur 9789819510085

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