Federated learning systematic review (6 résultats)

Etat : New.

Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 192 pp. Englisch.

Buch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Federated Learning (FL) represents a transformative leap in distributed machine learning by enabling multiple clients to collaboratively solve complex tasks without compromising data privacy. This innovative approach eliminates the need for centralized cloud storage, ensuring privacy-preserving data handling while offering smarter models, reduced latency, and enhanced power efficiency. This book serves as a comprehensive guide to the evolving field of Federated Learning, providing in-depth insights into its definition, architecture, and classification. It examines the distinctions between FL and traditional distributed learning paradigms through a comparative lens. The chapters explore key concepts, algorithmic advancements, and computational strategies that underpin the development of FL, with a particular focus on deep learning applications. Readers will find detailed discussions on critical topics such as horizontal and vertical FL, federated neural networks, federated reinforcement learning, and specialized algorithms like Federated LSTM and CNNs. By bridging theoretical foundations with practical implementations, the book also addresses common challenges in FL and presents potential pathways for future advancements. Aimed at researchers, academics, and practitioners, this book is valuable for understanding Federated Learning's role in shaping the future of privacy-conscious, intelligent machine learning systems.…

Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Federated Learning (FL) represents a transformative leap in distributed machine learning by enabling multiple clients to collaboratively solve complex tasks without compromising data privacy. This innovative approach eliminates the need for centralized cloud storage, ensuring privacy-preserving data handling while offering smarter models, reduced latency, and enhanced power efficiency. This book serves as a comprehensive guide to the evolving field of Federated Learning, providing in-depth insights into its definition, architecture, and classification. It examines the distinctions between FL and traditional distributed learning paradigms through a comparative lens. The chapters explore key concepts, algorithmic advancements, and computational strategies that underpin the development of FL, with a particular focus on deep learning applications. Readers will find detailed discussions on critical topics such as horizontal and vertical FL, federated neural networks, federated reinforcement learning, and specialized algorithms like Federated LSTM and CNNs. By bridging theoretical foundations with practical implementations, the book also addresses common challenges in FL and presents potential pathways for future advancements. Aimed at researchers, academics, and practitioners, this book is valuable for understanding Federated Learning's role in shaping the future of privacy-conscious, intelligent machine learning systems.Books on Demand GmbH, Überseering 33, 22297 Hamburg 192 pp. Englisch.…

Buch. Etat : Neu. Federated Learning - A Systematic Review | A Systematic Review | Andries Engelbrecht (u. a.) | Buch | Artificial Intelligence, Volume 33 | Englisch | 2025 | IntechOpen | EAN 9781836342120 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…