Federated learning for wireless networks
Recently machine learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently, wireless systems are mostly using machine learning schemes that are based on centralizing the training and inference processes by migrating the end-devices data to a...
| Main Author: | Hong, Choong Seon, |
|---|---|
| Other Authors: | Khan, Latif U.,, Chen, Mingzhe,, Chen, Dawei,, Saad, Walid,, Han, Zhu, 1974-, SpringerLink (Online service) |
| Format: | eBook |
| Language: | English |
| Published: |
Singapore :
Springer,
2021.
Singapore : 2021. |
| Physical Description: |
1 online resource. |
| Series: |
Wireless networks (Springer (Firm)).
|
| Subjects: |
| LEADER | 06103cam a2200853Ia 4500 | ||
|---|---|---|---|
| 001 | 1290840198 | ||
| 003 | OCoLC | ||
| 005 | 20240223121953.0 | ||
| 006 | m o d | ||
| 007 | cr un|---aucuu | ||
| 008 | 220105s2021 si ob 000 0 eng d | ||
| 019 | |a 1290813896 |a 1291315179 |a 1292518706 |a 1294360791 | ||
| 020 | |a 9789811649639 |q (electronic bk.) | ||
| 020 | |a 9811649634 |q (electronic bk.) | ||
| 020 | |z 9811649626 | ||
| 020 | |z 9789811649622 | ||
| 024 | 7 | |a 10.1007/978-981-16-4963-9 |2 doi | |
| 035 | |a (OCoLC)1290840198 |z (OCoLC)1290813896 |z (OCoLC)1291315179 |z (OCoLC)1292518706 |z (OCoLC)1294360791 | ||
| 037 | |b Springer | ||
| 040 | |a YDX |b eng |c YDX |d GW5XE |d EBLCP |d DKU |d OCLCO |d DCT |d OCLCF |d OCLCO |d UKAHL |d OCLCQ |d LIN |d AUD |d OCLCQ |d OCLCO |d OCLCQ |d OCLCO |d OCLCL | ||
| 049 | |a COM6 | ||
| 050 | 4 | |a Q325.5 | |
| 072 | 7 | |a UKN |2 bicssc | |
| 072 | 7 | |a COM075000 |2 bisacsh | |
| 072 | 7 | |a UKN |2 thema | |
| 082 | 0 | 4 | |a 006.3/1 |2 23 |
| 100 | 1 | |a Hong, Choong Seon, |e author. | |
| 245 | 1 | 0 | |a Federated learning for wireless networks / |c Choong Seon Hong, Latif U. Khan, Mingzhe Chen, Dawei Chen, Walid Saad, Zhu Han. |
| 260 | |a Singapore : |b Springer, |c 2021. | ||
| 264 | 1 | |a Singapore : |b Springer, |c 2021. | |
| 300 | |a 1 online resource. | ||
| 336 | |a text |b txt |2 rdacontent. | ||
| 337 | |a computer |b c |2 rdamedia. | ||
| 338 | |a online resource |b cr |2 rdacarrier. | ||
| 347 | |a text file |b PDF |2 rda. | ||
| 490 | 1 | |a Wireless networks, |x 2366-1445. | |
| 505 | 0 | |a Part 1 Fundamentals and Background -- 1 Introduction -- 2 Fundamentals of Federated Learning -- Part 2 Wireless Federated Learning: Design and Analysis 3 Resource Optimization for Wireless Federated Learning -- 4 Incentive Mechanisms for Federated Learning -- 5 Security and Privacy -- 6 Unsupervised Federated Learning -- Part 3 Federated Learning Applications in Wireless Networks -- 7 Wireless Virtual Reality -- 8 Vehicular Networks and Autonomous Driving Cars -- 9 Smart Industries and Intelligent Reflecting Surfaces. | |
| 520 | |a Recently machine learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently, wireless systems are mostly using machine learning schemes that are based on centralizing the training and inference processes by migrating the end-devices data to a third party centralized location. However, these schemes lead to end-devices privacy leakage. To address these issues, one can use a distributed machine learning at network edge. In this context, federated learning (FL) is one of most important distributed learning algorithm, allowing devices to train a shared machine learning model while keeping data locally. However, applying FL in wireless networks and optimizing the performance involves a range of research topics. For example, in FL, training machine learning models require communication between wireless devices and edge servers via wireless links. Therefore, wireless impairments such as uncertainties among wireless channel states, interference, and noise significantly affect the performance of FL. On the other hand, federated-reinforcement learning leverages distributed computation power and data to solve complex optimization problems that arise in various use cases, such as interference alignment, resource management, clustering, and network control. Traditionally, FL makes the assumption that edge devices will unconditionally participate in the tasks when invited, which is not practical in reality due to the cost of model training. As such, building incentive mechanisms is indispensable for FL networks. This book provides a comprehensive overview of FL for wireless networks. It is divided into three main parts: The first part briefly discusses the fundamentals of FL for wireless networks, while the second part comprehensively examines the design and analysis of wireless FL, covering resource optimization, incentive mechanism, security and privacy. It also presents several solutions based on optimization theory, graph theory, and game theory to optimize the performance of federated learning in wireless networks. Lastly, the third part describes several applications of FL in wireless networks. | ||
| 504 | |a Includes bibliographical references. | ||
| 588 | 0 | |a Online resource; title from PDF title page (SpringerLink, viewed January 11, 2022). | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Wireless communication systems. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Transmission sans fil. | |
| 650 | 7 | |a Machine learning. |2 fast. | |
| 650 | 7 | |a Wireless communication systems. |2 fast. | |
| 700 | 1 | |a Khan, Latif U., |e author. | |
| 700 | 1 | |a Chen, Mingzhe, |e author. | |
| 700 | 1 | |a Chen, Dawei, |e author. | |
| 700 | 1 | |a Saad, Walid, |e author. | |
| 700 | 1 | |a Han, Zhu, |d 1974- |e author. | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 776 | 0 | 8 | |c Original |z 9811649626 |z 9789811649622 |w (OCoLC)1258780850. |
| 830 | 0 | |a Wireless networks (Springer (Firm)). |x 2366-1445. | |
| 907 | |a .b65009071 |b multi |c - |d 220131 |e 240401 | ||
| 998 | |a (2)cue |a cu |b 240227 |c m |d z |e - |f eng |g si |h 0 |i 2 | ||
| 948 | |a MARCIVE Overnight, in 2024.03 | ||
| 948 | |a MARCIVE Comprehensive, in 2023.02 | ||
| 948 | |a MARCIVE Over, 01/2022 | ||
| 948 | |a MARCIVE Over, 02/2022 | ||
| 933 | |a Marcive found issue: "700 1 |a Khan, Latif U., |e author." | ||
| 994 | |a 92 |b COM | ||
| 995 | |a Loaded with m2btab.ltiac in 2024.03 | ||
| 995 | |a Loaded with m2btab.elec in 2024.02 | ||
| 995 | |a Loaded with m2btab.ltiac in 2023.02 | ||
| 995 | |a Loaded with m2btab.ltiac in 2022.02 | ||
| 995 | |a Loaded with m2btab.ltiac in 2022.02 | ||
| 995 | |a Loaded with m2btab.elec in 2022.01 | ||
| 995 | |a Loaded with m2btab.auth in 2022.02 | ||
| 995 | |a Loaded with m2btab.auth in 2022.03 | ||
| 995 | |a Loaded with m2btab.auth in 2024.03 | ||
| 999 | |e z | ||
| 999 | |a cue | ||
| 989 | |d cueme |e - - |f - - |g - |h 0 |i 0 |j 200 |k 240227 |l $0.00 |m |n - - |o - |p 0 |q 0 |t 0 |x 0 |w SpringerLink |1 .i150771563 |u http://ezproxy.coloradomesa.edu/login?url=https://link.springer.com/10.1007/978-981-16-4963-9 |3 SpringerLink |z Click here for access | ||