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Federated Learning Systems

Towards Next-Generation AI

Mohamed Medhat Gaber editor Muhammad Habib ur Rehman editor

Format:Paperback

Publisher:Springer Nature Switzerland AG

Published:12th Jun '22

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Federated Learning Systems cover

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.

ISBN: 9783030706067

Dimensions: unknown

Weight: 332g

196 pages

1st ed. 2021