Federated Learning For Future Intelligent Wireless Networks

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Federated Learning for Future Intelligent Wireless Networks

Author : Yao Sun,Chaoqun You,Gang Feng,Lei Zhang
Publisher : John Wiley & Sons
Page : 324 pages
File Size : 44,8 Mb
Release : 2023-12-04
Category : Technology & Engineering
ISBN : 9781119913917

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Federated Learning for Future Intelligent Wireless Networks by Yao Sun,Chaoqun You,Gang Feng,Lei Zhang Pdf

Federated Learning for Future Intelligent Wireless Networks Explore the concepts, algorithms, and applications underlying federated learning In Federated Learning for Future Intelligent Wireless Networks, a team of distinguished researchers deliver a robust and insightful collection of resources covering the foundational concepts and algorithms powering federated learning, as well as explanations of how they can be used in wireless communication systems. The editors have included works that examine how communication resource provision affects federated learning performance, accuracy, convergence, scalability, and security and privacy. Readers will explore a wide range of topics that show how federated learning algorithms, concepts, and design and optimization issues apply to wireless communications. Readers will also find: A thorough introduction to the fundamental concepts and algorithms of federated learning, including horizontal, vertical, and hybrid FL Comprehensive explorations of wireless communication network design and optimization for federated learning Practical discussions of novel federated learning algorithms and frameworks for future wireless networks Expansive case studies in edge intelligence, autonomous driving, IoT, MEC, blockchain, and content caching and distribution Perfect for electrical and computer science engineers, researchers, professors, and postgraduate students with an interest in machine learning, Federated Learning for Future Intelligent Wireless Networks will also benefit regulators and institutional actors responsible for overseeing and making policy in the area of artificial intelligence.

Federated Learning for Future Intelligent Wireless Networks

Author : Yao Sun,Chaoqun You,Gang Feng,Lei Zhang
Publisher : John Wiley & Sons
Page : 324 pages
File Size : 46,7 Mb
Release : 2023-12-27
Category : Technology & Engineering
ISBN : 9781119913894

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Federated Learning for Future Intelligent Wireless Networks by Yao Sun,Chaoqun You,Gang Feng,Lei Zhang Pdf

Federated Learning for Future Intelligent Wireless Networks Explore the concepts, algorithms, and applications underlying federated learning In Federated Learning for Future Intelligent Wireless Networks, a team of distinguished researchers deliver a robust and insightful collection of resources covering the foundational concepts and algorithms powering federated learning, as well as explanations of how they can be used in wireless communication systems. The editors have included works that examine how communication resource provision affects federated learning performance, accuracy, convergence, scalability, and security and privacy. Readers will explore a wide range of topics that show how federated learning algorithms, concepts, and design and optimization issues apply to wireless communications. Readers will also find: A thorough introduction to the fundamental concepts and algorithms of federated learning, including horizontal, vertical, and hybrid FL Comprehensive explorations of wireless communication network design and optimization for federated learning Practical discussions of novel federated learning algorithms and frameworks for future wireless networks Expansive case studies in edge intelligence, autonomous driving, IoT, MEC, blockchain, and content caching and distribution Perfect for electrical and computer science engineers, researchers, professors, and postgraduate students with an interest in machine learning, Federated Learning for Future Intelligent Wireless Networks will also benefit regulators and institutional actors responsible for overseeing and making policy in the area of artificial intelligence.

Federated Learning for Wireless Networks

Author : Choong Seon Hong,Latif U. Khan,Mingzhe Chen,Dawei Chen,Walid Saad,Zhu Han
Publisher : Springer Nature
Page : 257 pages
File Size : 50,7 Mb
Release : 2022-01-01
Category : Computers
ISBN : 9789811649639

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Federated Learning for Wireless Networks by Choong Seon Hong,Latif U. Khan,Mingzhe Chen,Dawei Chen,Walid Saad,Zhu Han Pdf

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.

Machine Learning for Future Wireless Communications

Author : Fa-Long Luo
Publisher : John Wiley & Sons
Page : 490 pages
File Size : 46,5 Mb
Release : 2020-02-10
Category : Technology & Engineering
ISBN : 9781119562252

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Machine Learning for Future Wireless Communications by Fa-Long Luo Pdf

A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities. Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author – a noted expert on the topic – covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource: Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks Covers a range of topics from architecture and optimization to adaptive resource allocations Reviews state-of-the-art machine learning based solutions for network coverage Includes an overview of the applications of machine learning algorithms in future wireless networks Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.

Federated Learning Over Wireless Edge Networks

Author : Wei Yang Bryan Lim,Jer Shyuan Ng,Zehui Xiong,Dusit Niyato,Chunyan Miao
Publisher : Springer Nature
Page : 175 pages
File Size : 41,6 Mb
Release : 2022-09-28
Category : Technology & Engineering
ISBN : 9783031078385

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Federated Learning Over Wireless Edge Networks by Wei Yang Bryan Lim,Jer Shyuan Ng,Zehui Xiong,Dusit Niyato,Chunyan Miao Pdf

This book first presents a tutorial on Federated Learning (FL) and its role in enabling Edge Intelligence over wireless edge networks. This provides readers with a concise introduction to the challenges and state-of-the-art approaches towards implementing FL over the wireless edge network. Then, in consideration of resource heterogeneity at the network edge, the authors provide multifaceted solutions at the intersection of network economics, game theory, and machine learning towards improving the efficiency of resource allocation for FL over the wireless edge networks. A clear understanding of such issues and the presented theoretical studies will serve to guide practitioners and researchers in implementing resource-efficient FL systems and solving the open issues in FL respectively.

Data-Driven Intelligence in Wireless Networks

Author : Muhammad Khalil Afzal,Muhammad Ateeq,Sung Won Kim
Publisher : CRC Press
Page : 267 pages
File Size : 43,5 Mb
Release : 2023-03-27
Category : Computers
ISBN : 9781000841336

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Data-Driven Intelligence in Wireless Networks by Muhammad Khalil Afzal,Muhammad Ateeq,Sung Won Kim Pdf

Covers details on wireless communication problems, conducive for data-driven solutions Provides a comprehensive account of programming languages, tools, techniques, and good practices Provides an introduction to data-driven techniques applied to wireless communication systems Examines data-driven techniques, performance, and design issues in wireless networks Includes several case studies that examine data-driven solution for QoS in heterogeneous wireless networks

Future Wireless Networks and Information Systems

Author : Ying Zhang
Publisher : Springer Science & Business Media
Page : 762 pages
File Size : 44,8 Mb
Release : 2012-02-01
Category : Technology & Engineering
ISBN : 9783642273230

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Future Wireless Networks and Information Systems by Ying Zhang Pdf

This volume contains revised and extended research articles written by prominent researchers participating in ICFWI 2011 conference. The 2011 International Conference on Future Wireless Networks and Information Systems (ICFWI 2011) has been held on November 30 ~ December 1, 2011, Macao, China. Topics covered include Wireless Information Networks, Wireless Networking Technologies, Mobile Software and Services, intelligent computing, network management, power engineering, control engineering, Signal and Image Processing, Machine Learning, Control Systems and Applications, The book will offer the states of arts of tremendous advances in Wireless Networks and Information Systems and also serve as an excellent reference work for researchers and graduate students working on Wireless Networks and Information Systems.

Towards User-Centric Intelligent Network Selection in 5G Heterogeneous Wireless Networks

Author : Zhiyong Du,Bin Jiang,Qihui Wu,Yuhua Xu,Kun Xu
Publisher : Springer Nature
Page : 136 pages
File Size : 42,5 Mb
Release : 2019-11-06
Category : Technology & Engineering
ISBN : 9789811511202

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Towards User-Centric Intelligent Network Selection in 5G Heterogeneous Wireless Networks by Zhiyong Du,Bin Jiang,Qihui Wu,Yuhua Xu,Kun Xu Pdf

This book presents reinforcement learning (RL) based solutions for user-centric online network selection optimization. The main content can be divided into three parts. The first part (chapter 2 and 3) focuses on how to learning the best network when QoE is revealed beyond QoS under the framework of multi-armed bandit (MAB). The second part (chapter 4 and 5) focuses on how to meet dynamic user demand in complex and uncertain heterogeneous wireless networks under the framework of markov decision process (MDP). The third part (chapter 6 and 7) focuses on how to meet heterogeneous user demand for multiple users inlarge-scale networks under the framework of game theory. Efficient RL algorithms with practical constraints and considerations are proposed to optimize QoE for realizing intelligent online network selection for future mobile networks. This book is intended as a reference resource for researchers and designers in resource management of 5G networks and beyond.

Future Wireless Networks and Information Systems

Author : Ying Zhang
Publisher : Springer Science & Business Media
Page : 764 pages
File Size : 49,6 Mb
Release : 2012-01-25
Category : Technology & Engineering
ISBN : 9783642273261

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Future Wireless Networks and Information Systems by Ying Zhang Pdf

This volume contains revised and extended research articles written by prominent researchers participating in the ICF4C 2011 conference. 2011 International Conference on Future Communication, Computing, Control and Management (ICF4C 2011) has been held on December 16-17, 2011, Phuket, Thailand. Topics covered include intelligent computing, network management, wireless networks, telecommunication, power engineering, control engineering, Signal and Image Processing, Machine Learning, Control Systems and Applications, The book will offer the states of arts of tremendous advances in Computing, Communication, Control, and Management and also serve as an excellent reference work for researchers and graduate students working on Computing, Communication, Control, and Management Research.

Machine Learning and Wireless Communications

Author : Yonina C. Eldar,Andrea Goldsmith,Deniz Gündüz,H. Vincent Poor
Publisher : Cambridge University Press
Page : 560 pages
File Size : 51,5 Mb
Release : 2022-06-30
Category : Technology & Engineering
ISBN : 9781108967730

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Machine Learning and Wireless Communications by Yonina C. Eldar,Andrea Goldsmith,Deniz Gündüz,H. Vincent Poor Pdf

How can machine learning help the design of future communication networks – and how can future networks meet the demands of emerging machine learning applications? Discover the interactions between two of the most transformative and impactful technologies of our age in this comprehensive book. First, learn how modern machine learning techniques, such as deep neural networks, can transform how we design and optimize future communication networks. Accessible introductions to concepts and tools are accompanied by numerous real-world examples, showing you how these techniques can be used to tackle longstanding problems. Next, explore the design of wireless networks as platforms for machine learning applications – an overview of modern machine learning techniques and communication protocols will help you to understand the challenges, while new methods and design approaches will be presented to handle wireless channel impairments such as noise and interference, to meet the demands of emerging machine learning applications at the wireless edge.

Green Machine Learning Protocols for Future Communication Networks

Author : Saim Ghafoor,Mubashir Husain Rehmani
Publisher : CRC Press
Page : 249 pages
File Size : 46,6 Mb
Release : 2023-10-25
Category : Computers
ISBN : 9781000968934

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Green Machine Learning Protocols for Future Communication Networks by Saim Ghafoor,Mubashir Husain Rehmani Pdf

Machine learning has shown tremendous benefits in solving complex network problems and providing situation and parameter prediction. However, heavy resources are required to process and analyze the data, which can be done either offline or using edge computing but also requires heavy transmission resources to provide a timely response. The need here is to provide lightweight machine learning protocols that can process and analyze the data at run time and provide a timely and efficient response. These algorithms have grown in terms of computation and memory requirements due to the availability of large data sets. These models/algorithms also require high levels of resources such as computing, memory, communication, and storage. The focus so far was on producing highly accurate models for these communication networks without considering the energy consumption of these machine learning algorithms. For future scalable and sustainable network applications, efforts are required toward designing new machine learning protocols and modifying the existing ones, which consume less energy, i.e., green machine learning protocols. In other words, novel and lightweight green machine learning algorithms/protocols are required to reduce energy consumption which can also reduce the carbon footprint. To realize the green machine learning protocols, this book presents different aspects of green machine learning for future communication networks. This book highlights mainly the green machine learning protocols for cellular communication, federated learning-based models, and protocols for Beyond Fifth Generation networks, approaches for cloud-based communications, and Internet-of-Things. This book also highlights the design considerations and challenges for green machine learning protocols for different future applications.

Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems

Author : K. Suganthi,R. Karthik,G. Rajesh,Peter Ho Chiung Ching
Publisher : CRC Press
Page : 296 pages
File Size : 49,7 Mb
Release : 2021-09-14
Category : Technology & Engineering
ISBN : 9781000441819

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Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems by K. Suganthi,R. Karthik,G. Rajesh,Peter Ho Chiung Ching Pdf

This book offers the latest advances and results in the fields of Machine Learning and Deep Learning for Wireless Communication and provides positive and critical discussions on the challenges and prospects. It provides a broad spectrum in understanding the improvements in Machine Learning and Deep Learning that are motivating by the specific constraints posed by wireless networking systems. The book offers an extensive overview on intelligent Wireless Communication systems and its underlying technologies, research challenges, solutions, and case studies. It provides information on intelligent wireless communication systems and its models, algorithms and applications. The book is written as a reference that offers the latest technologies and research results to various industry problems.

Next-Generation Wireless Networks Meet Advanced Machine Learning Applications

Author : Com?a, Ioan-Sorin,Trestian, Ramona
Publisher : IGI Global
Page : 356 pages
File Size : 41,8 Mb
Release : 2019-01-25
Category : Technology & Engineering
ISBN : 9781522574590

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Next-Generation Wireless Networks Meet Advanced Machine Learning Applications by Com?a, Ioan-Sorin,Trestian, Ramona Pdf

The ever-evolving wireless technology industry is demanding new technologies and standards to ensure a higher quality of experience for global end-users. This developing challenge has enabled researchers to identify the present trend of machine learning as a possible solution, but will it meet business velocity demand? Next-Generation Wireless Networks Meet Advanced Machine Learning Applications is a pivotal reference source that provides emerging trends and insights into various technologies of next-generation wireless networks to enable the dynamic optimization of system configuration and applications within the fields of wireless networks, broadband networks, and wireless communication. Featuring coverage on a broad range of topics such as machine learning, hybrid network environments, wireless communications, and the internet of things; this publication is ideally designed for industry experts, researchers, students, academicians, and practitioners seeking current research on various technologies of next-generation wireless networks.

Machine Learning for Future Wireless Communications

Author : Fa-Long Luo
Publisher : Wiley-IEEE Press
Page : 496 pages
File Size : 43,5 Mb
Release : 2019-12-13
Category : Electronic
ISBN : 1119562309

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Machine Learning for Future Wireless Communications by Fa-Long Luo Pdf

A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities. Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author - a noted expert on the topic - covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource: Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks Covers a range of topics from architecture and optimization to adaptive resource allocations Reviews state-of-the-art machine learning based solutions for network coverage Includes an overview of the applications of machine learning algorithms in future wireless networks Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.