Federated Learning Systems | Taschenbuch

Cover: 9783030706067 | Federated Learning Systems | Towards Next-Generation AI | Taschenbuch

Rückseite: 9783030706067 | Federated Learning Systems | Towards Next-Generation AI | Taschenbuch

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Sprache: Englisch

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Beschreibung

This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors¿ control of their critical data.

Zusammenfassung

Presents advances in federated learning

Shows how federated learning can transform next-generation artificial intelligence applications

Proposes solutions to address key federated learning challenges

Inhaltsverzeichnis

Federated Learning Research: Trends and Bibliometric Analysis.- A Review of Privacy-preserving Federated Learning for the Internet-of-Things.- Di¿erentially Private Federated Learning: Algorithm, Analysis and Optimization.- Advancements of federated learning towards privacy preservation: from federated learning to split learning.- PySyft: A Library for Easy Federated Learning.- Federated Learning Systems for Healthcare: Perspective and Recent Progress.- Towards Blockchain-Based Fair and Trustworthy Federated Learning Systems.- An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies.

Erscheinungsjahr:	2022
Fachbereich:	Technik allgemein
Genre:	Mathematik , Medizin , Naturwissenschaften , Technik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Inhalt:	xvi 196 S. 3 s/w Illustr. 42 farbige Illustr. 196 p. 45 illus. 42 illus. in color.
ISBN-13:	9783030706067
ISBN-10:	3030706060
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Redaktion:	Gaber, Mohamed Medhat Rehman, Muhammad Habib Ur
Herausgeber:	Muhammad Habib ur Rehman/Mohamed Medhat Gaber
Auflage:	1st edition 2021
Hersteller:	Springer International Publishing
Verantwortliche Person für die EU:	Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße:	235 x 155 x 12 mm
Von/Mit:	Mohamed Medhat Gaber (u. a.)
Erscheinungsdatum:	12.06.2022
Gewicht:	0,33 kg

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