Federated Learning For Wireless Networks
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Federated Learning For Wireless Networks
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Author : Choong Seon Hong
language : en
Publisher: Springer Nature
Release Date : 2022-01-01
Federated Learning For Wireless Networks written by Choong Seon Hong and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-01 with Computers categories.
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.
Communication Efficient Federated Learning For Wireless Networks
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Author : Mingzhe Chen
language : en
Publisher: Springer Nature
Release Date :
Communication Efficient Federated Learning For Wireless Networks written by Mingzhe Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Federated Learning For Future Intelligent Wireless Networks
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Author : Yao Sun
language : en
Publisher: John Wiley & Sons
Release Date : 2023-12-04
Federated Learning For Future Intelligent Wireless Networks written by Yao Sun and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-04 with Technology & Engineering categories.
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.
Communication Efficient Federated Learning For Wireless Networks
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Author : Mingzhe Chen
language : en
Publisher: Springer
Release Date : 2024-02-29
Communication Efficient Federated Learning For Wireless Networks written by Mingzhe Chen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-29 with Computers categories.
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. Machine learning and data-driven approaches have recently received considerable attention as key enablers for next-generation intelligent networks. Currently, most existing learning solutions for wireless networks rely on centralizing the training and inference processes by uploading data generated at edge devices to data centers. However, such a centralized paradigm may lead to privacy leakage, violate the latency constraints of mobile applications, or may be infeasible due to limited bandwidth or power constraints of edge devices. To address these issues, distributing machine learning at the network edge provides a promising solution, where edge devices collaboratively train a shared model using real-time generated mobile data. The avoidance of data uploading to a central server not only helps preserve privacy but also reduces network traffic congestion as well as communication cost. Federated learning (FL) is one of most important distributed learning algorithms. In particular, FL enables devices to train a shared machine learning model while keeping data locally. However, in FL, training machine learning models requires communication between wireless devices and edge servers over wireless links. Therefore, wireless impairments such as noise, interference, and uncertainties among wireless channel states will significantly affect the training process and performance of FL. For example, transmission delay can significantly impact the convergence time of FL algorithms. In consequence, it is necessary to optimize wireless network performance for the implementation of FL algorithms. 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.
Federated Learning Over Wireless Edge Networks
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Author : Wei Yang Bryan Lim
language : en
Publisher: Springer Nature
Release Date : 2022-09-28
Federated Learning Over Wireless Edge Networks written by Wei Yang Bryan Lim and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-28 with Technology & Engineering categories.
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.
Machine Learning And Wireless Communications
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Author : Yonina C. Eldar
language : en
Publisher: Cambridge University Press
Release Date : 2022-06-30
Machine Learning And Wireless Communications written by Yonina C. Eldar and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-30 with Technology & Engineering categories.
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.
Data Driven Intelligence In Wireless Networks
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Author : Muhammad Khalil Afzal
language : en
Publisher: CRC Press
Release Date : 2023-03-27
Data Driven Intelligence In Wireless Networks written by Muhammad Khalil Afzal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-27 with Computers categories.
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
6g Mobile Wireless Networks
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Author : Yulei Wu
language : en
Publisher: Springer Nature
Release Date : 2021-08-24
6g Mobile Wireless Networks written by Yulei Wu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-24 with Computers categories.
This book is the world’s first book on 6G Mobile Wireless Networks that aims to provide a comprehensive understanding of key drivers, use cases, research requirements, challenges and open issues that are expected to drive 6G research. In this book, we have invited world-renowned experts from industry and academia to share their thoughts on different aspects of 6G research. Specifically, this book covers the following topics: 6G Use Cases, Requirements, Metrics and Enabling Technologies, PHY Technologies for 6G Wireless, Reconfigurable Intelligent Surface for 6G Wireless Networks, Millimeter-wave and Terahertz Spectrum for 6G Wireless, Challenges in Transport Layer for Tbit/s Communications, High-capacity Backhaul Connectivity for 6G Wireless, Cloud Native Approach for 6G Wireless Networks, Machine Type Communications in 6G, Edge Intelligence and Pervasive AI in 6G, Blockchain: Foundations and Role in 6G, Role of Open-source Platforms in 6G, and Quantum Computing and 6G Wireless. The overarching aim of this book is to explore the evolution from current 5G networks towards the future 6G networks from a service, air interface and network perspective, thereby laying out a vision for 6G networks. This book not only discusses the potential 6G use cases, requirements, metrics and enabling technologies, but also discusses the emerging technologies and topics such as 6G PHY technologies, reconfigurable intelligent surface, millimeter-wave and THz communications, visible light communications, transport layer for Tbit/s communications, high-capacity backhaul connectivity, cloud native approach, machine-type communications, edge intelligence and pervasive AI, network security and blockchain, and the role of open-source platform in 6G. This book provides a systematic treatment of the state-of-the-art in these emerging topics and their role in supporting a wide variety of verticals in the future. As such, it provides a comprehensive overview of the expected applications of 6G with a detailed discussion of their requirements and possible enabling technologies. This book also outlines the possible challenges and research directions to facilitate the future research and development of 6G mobile wireless networks.
Federated Learning For Iot Applications
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Author : Satya Prakash Yadav
language : en
Publisher: Springer Nature
Release Date : 2022-02-02
Federated Learning For Iot Applications written by Satya Prakash Yadav and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-02 with Technology & Engineering categories.
This book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy. The authors first show how federated learning provides a unique way to build personalized models using data without intruding on users’ privacy. The authors then provide a comprehensive survey of state-of-the-art research on federated learning, giving the reader a general overview of the field. The book also investigates how a personalized federated learning framework is needed in cloud-edge architecture as well as in wireless-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, the book investigates emerging personalized federated learning methods that are able to mitigate the negative effects caused by heterogeneities in different aspects. The book provides case studies of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications, as well as multiple controller design and system analysis tools including model predictive control, linear matrix inequalities, optimal control, etc. This unique and complete co-design framework will benefit researchers, graduate students and engineers in the fields of control theory and engineering.
Machine Learning And Wireless Communications
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Author : Yonina C. Eldar
language : en
Publisher: Cambridge University Press
Release Date : 2022-08-04
Machine Learning And Wireless Communications written by Yonina C. Eldar and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-04 with Computers categories.
Discover connections between these transformative and impactful technologies, through comprehensive introductions and real-world examples.