Articles liés à Foundations of Federated Learning
Synopsis
This book provides a rigorous yet accessible introduction to federated learning as a privacy-aware, decentralized, and trustworthy approach to artificial intelligence. As data becomes increasingly distributed across devices, organizations, institutions, and jurisdictions, conventional centralized machine learning faces growing challenges related to privacy, regulation, communication costs, data ownership, and trust. The book explains the mathematical foundations, optimization methods, system architectures, and key algorithms that enable collaborative model training without centralizing sensitive data. It also examines heterogeneous data, scalability, fault tolerance, privacy and security mechanisms, governance, fairness, accountability, and regulatory compliance. Suitable for students, researchers, educators, and practitioners, it offers a unified framework for designing responsible, secure, and privacy-preserving AI systems worldwide.
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Foundations of Federated Learning
Edité par
Springer Nature Switzerland AG Feb 2027, 2027
ISBN 10 : 3032340837
ISBN 13 : 9783032340832
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a rigorous yet accessible introduction to federated learning as a privacy-aware, decentralized, and trustworthy approach to artificial intelligence. As data becomes increasingly distributed across devices, organizations, institutions, and jurisdictions, conventional centralized machine learning faces growing challenges related to privacy, regulation, communication costs, data ownership, and trust. The book explains the mathematical foundations, optimization methods, system architectures, and key algorithms that enable collaborative model training without centralizing sensitive data. It also examines heterogeneous data, scalability, fault tolerance, privacy and security mechanisms, governance, fairness, accountability, and regulatory compliance. Suitable for students, researchers, educators, and practitioners, it offers a unified framework for designing responsible, secure, and privacy-preserving AI systems worldwide. 780 pp. Englisch. N° de réf. du vendeur 9783032340832
Acheter neuf
EUR 80,24
Expédition à EUR 23
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Foundations of Federated Learning
Edité par
Springer Nature Switzerland AG, 2027
ISBN 10 : 3032340837
ISBN 13 : 9783032340832
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a rigorous yet accessible introduction to federated learning as a privacy-aware, decentralized, and trustworthy approach to artificial intelligence. As data becomes increasingly distributed across devices, organizations, institutions, and jurisdictions, conventional centralized machine learning faces growing challenges related to privacy, regulation, communication costs, data ownership, and trust. The book explains the mathematical foundations, optimization methods, system architectures, and key algorithms that enable collaborative model training without centralizing sensitive data. It also examines heterogeneous data, scalability, fault tolerance, privacy and security mechanisms, governance, fairness, accountability, and regulatory compliance. Suitable for students, researchers, educators, and practitioners, it offers a unified framework for designing responsible, secure, and privacy-preserving AI systems worldwide. N° de réf. du vendeur 9783032340832
Acheter neuf
EUR 93,35
Expédition à EUR 44,17
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 2 disponible(s)