Federated learning systems : towards next generation AI / Muhammad Habib ur Rehman, Mohamed Medhat Gaber, editors.
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 clou...
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| Other Authors: | , |
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| Format: | Ebook |
| Language: | English |
| Published: |
Cham :
Springer,
[2021]
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| Series: | Studies in computational intelligence ;
v. 965. |
| Subjects: | |
| Online Access: | Springer eBooks |
| Summary: | 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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| Physical Description: | 1 online resource : illustrations (chiefly colour). |
| ISBN: | 3030706036 9783030706036 3030706044 9783030706043 |
| ISSN: | 1860-949X ; |