Federated Learning Systems - Towards Next-Generation AI (Paperback, 1st ed. 2021)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Studies in Computational Intelligence, 965
Release date:	June 2022
First published:	2021
Editors:	Muhammad Habibur Rehman • Mohamed Medhat Gaber
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	196
Edition:	1st ed. 2021
ISBN-13:	978-3-03-070606-7
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-03-070606-0
Barcode:	9783030706067

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