| 書(shū)目名稱 | Federated Learning Systems |
| 副標(biāo)題 | Towards Next-Generat |
| 編輯 | Muhammad Habib ur Rehman,Mohamed Medhat Gaber |
| 視頻video | http://file.papertrans.cn/342/341595/341595.mp4 |
| 概述 | Presents advances in federated learning.Shows how federated learning can transform next-generation artificial intelligence applications.Proposes solutions to address key federated learning challenges |
| 叢書(shū)名稱 | Studies in Computational Intelligence |
| 圖書(shū)封面 |  |
| 描述 | 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.. |
| 出版日期 | Book 2021 |
| 關(guān)鍵詞 | Deep Learning; Differential Privacy; Distributed Machine Learning; Federated Learning; Fine-grained Fede |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-70604-3 |
| isbn_softcover | 978-3-030-70606-7 |
| isbn_ebook | 978-3-030-70604-3Series ISSN 1860-949X Series E-ISSN 1860-9503 |
| issn_series | 1860-949X |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
|Archiver|手機(jī)版|小黑屋|
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