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Federated Learning Systems: Towards Next-Generation AI (Studies in Computational Intelligence, 965, Band 965) - Hardcover

Buch 469 von 538: Studies in Computational Intelligence
 
9783030706036: Federated Learning Systems: Towards Next-Generation AI (Studies in Computational Intelligence, 965, Band 965)
Hardcover
ISBN 10: 3030706036 ISBN 13: 9783030706036
Verlag: Springer, 2021

Inhaltsangabe

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.

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Von der hinteren Coverseite

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.

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Verlag
Springer
Erscheinungsdatum
2021
Sprache
Englisch
ISBN 10 
3030706036
ISBN 13 
9783030706036
Einband
Gebundene Ausgabe
Auflage
1
Anzahl der Seiten
212
Herausgeber
Rehman Muhammad Habib Ur, Gaber Mohamed Medhat
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