Communication Efficient Federated Learning for Wireless Networks / Wireless Networks (PDF)
(Sprache: Englisch)
This book provides a comprehensive study of Federated Learning (FL) over wireless networks. It consists of three main parts: (a) Fundamentals and preliminaries of FL, (b) analysis and optimization of FL over wireless networks, and (c) applications of...
sofort als Download lieferbar
eBook (pdf)
Fr. 177.00
inkl. MwSt.
- Kreditkarte, Paypal, Rechnung
- Kostenloser tolino webreader
Produktdetails
Produktinformationen zu „Communication Efficient Federated Learning for Wireless Networks / Wireless Networks (PDF)“
This book provides a comprehensive study of Federated Learning (FL) over wireless networks. It consists of three main parts: (a) Fundamentals and preliminaries of FL, (b) analysis and optimization of FL over wireless networks, and (c) applications of wireless FL for Internet-of-Things systems. In particular, in the first part, the authors provide a detailed overview on widely-studied FL framework. In the second part of this book, the authors comprehensively discuss three key wireless techniques including wireless resource management, quantization, and over-the-air computation to support the deployment of FL over realistic wireless networks. It also presents several solutions based on optimization theory, graph theory and machine learning to optimize the performance of FL over wireless networks. In the third part of this book, the authors introduce the use of wireless FL algorithms for autonomous vehicle control and mobile edge computing optimization.
This book targets researchers and advanced level students in computer science and electrical engineering. Professionals working in signal processing and machine learning will also buy this book.
Autoren-Porträt von Mingzhe Chen, Shuguang Cui
Mingzhe Chen (S'15-M'19) is currently an Assistant Professor with the Department of Electrical and Computer Engineering and Institute of Data Science and Computing at University of Miami. His research interests include federated learning, reinforcement learning, virtual reality, unmanned aerial vehicles, and Internet of Things. He has received four IEEE Communication Society journal paper awards including the IEEE Marconi Prize Paper Award in Wireless Communications in 2023, the Young Author Best Paper Award in 2021 and 2023, and the Fred W. Ellersick Prize Award in 2022, and four conference best paper awards at ICCCN in 2023, IEEE WCNC in 2021, IEEE ICC in 2020, and IEEE GLOBECOM in 2020. He currently serves as an Associate Editor of IEEE Transactions on Mobile Computing, IEEE Wireless Communications Letters, IEEE Transactions on Green Communications and Networking, and IEEE Transactions on Machine Learning in Communications and Networking.Shuguang Cui (S'99-M'05-SM'12-F'14) received his Ph.D in Electrical Engineering from Stanford University, California, USA, in 2005. Afterwards, he has been working as assistant, associate, full, Chair Professor in Electrical and Computer Engineering at the Univ. of Arizona, Texas A&M University, UC Davis, and CUHK at Shenzhen respectively. He has also served as the Executive Dean for the School of Science and Engineering at CUHK, Shenzhen, the Executive Vice Director at Shenzhen Research Institute of Big Data, and the Director for Future Network of Intelligence Institute (FNii). His current research interests focus on the merging between AI and communication neworks. He was selected as the Thomson Reuters Highly Cited Researcher and listed in the Worlds' Most Influential Scientific Minds by ScienceWatch in 2014. He was the recipient of the IEEE Signal Processing Society 2012 Best Paper Award. He has served as the general co-chair and TPC co-chairs for many IEEE conferences. He has also been serving as the area editor for IEEE
... mehr
Signal Processing Magazine, and associate editors for IEEE Transactions on Big Data, IEEE Transactions on Signal Processing, IEEE JSAC Series on Green Communications and Networking, and IEEE Transactions on Wireless Communications. He has been the elected member for IEEE Signal Processing Society SPCOM Technical Committee (2009~2014) and the elected Chair for IEEE ComSoc Wireless Technical Committee (2017~2018). He is a member of the Steering Committee for IEEE Transactions on Big Data and the Chair of the Steering Committee for IEEE Transactions on Cognitive Communications and Networking. He is also the Vice Chair of the IEEE VT Fellow Evaluation Committee and a member of the IEEE ComSoc Award Committee. He was elected as an IEEE Fellow in 2013, an IEEE ComSoc Distinguished Lecturer in 2014, and IEEE VT Society Distinguished Lecturer in 2019. In 2020, he won the IEEE ICC best paper award, ICIP best paper finalist, the IEEE Globecom best paper award. In 2021, he won the IEEE WCNC best paper award. In 2023, he won the IEEE Marconi Best Paper Award, got elected as a Fellow of both Canadian Academy of Engineering and the Royal Society of Canada, and starts to serve as the Editor-in-Chief for IEEE Transactions on Mobile Computing.
... weniger
Bibliographische Angaben
- Autoren: Mingzhe Chen , Shuguang Cui
- 2024, 1st ed. 2024, 179 Seiten, Englisch
- Verlag: Springer International Publishing
- ISBN-10: 3031512669
- ISBN-13: 9783031512667
- Erscheinungsdatum: 19.02.2024
Abhängig von Bildschirmgrösse und eingestellter Schriftgrösse kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: PDF
- Grösse: 6.52 MB
- Ohne Kopierschutz
- Vorlesefunktion
Sprache:
Englisch
Kommentar zu "Communication Efficient Federated Learning for Wireless Networks / Wireless Networks"
0 Gebrauchte Artikel zu „Communication Efficient Federated Learning for Wireless Networks / Wireless Networks“
| Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
|---|
Schreiben Sie einen Kommentar zu "Communication Efficient Federated Learning for Wireless Networks / Wireless Networks".
Kommentar verfassen