Federated Learning Over Wireless Edge Networks

Author : Wei Yang Bryan Lim
Publisher : Springer Nature
ISBN 13 : 3031078381
Total Pages : 175 pages
Book Rating : 4.85/5 ( download)

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Download or read book Federated Learning Over Wireless Edge Networks written by Wei Yang Bryan Lim and published by Springer Nature. This book was released on 2022-09-28 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Choong Seon Hong
Publisher : Springer Nature
ISBN 13 : 9811649634
Total Pages : 257 pages
Book Rating : 4.39/5 ( download)

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Download or read book Federated Learning for Wireless Networks written by Choong Seon Hong and published by Springer Nature. This book was released on 2022-01-01 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Mingzhe Chen
Publisher : Springer Nature
ISBN 13 : 3031512669
Total Pages : 189 pages
Book Rating : 4.67/5 ( download)

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Download or read book Communication Efficient Federated Learning for Wireless Networks written by Mingzhe Chen and published by Springer Nature. This book was released on with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Songze Li
Publisher :
ISBN 13 : 9781680837056
Total Pages : 148 pages
Book Rating : 4.52/5 ( download)

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Download or read book Coded Computing written by Songze Li and published by . This book was released on 2020 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: We introduce the concept of “coded computing”, a novel computing paradigm that utilizes coding theory to effectively inject and leverage data/computation redundancy to mitigate several fundamental bottlenecks in large-scale distributed computing, namely communication bandwidth, straggler’s (i.e., slow or failing nodes) delay, privacy and security bottlenecks.

Author : Yao Sun
Publisher : John Wiley & Sons
ISBN 13 : 1119913918
Total Pages : 324 pages
Book Rating : 4.17/5 ( download)

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Download or read book Federated Learning for Future Intelligent Wireless Networks written by Yao Sun and published by John Wiley & Sons. This book was released on 2023-12-04 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Qiang Qiang Yang
Publisher : Springer Nature
ISBN 13 : 3031015851
Total Pages : 189 pages
Book Rating : 4.54/5 ( download)

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Download or read book Federated Learning written by Qiang Qiang Yang and published by Springer Nature. This book was released on 2022-06-01 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.

Author : Yonina C. Eldar
Publisher : Cambridge University Press
ISBN 13 : 1108967736
Total Pages : 560 pages
Book Rating : 4.30/5 ( download)

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Download or read book Machine Learning and Wireless Communications written by Yonina C. Eldar and published by Cambridge University Press. This book was released on 2022-06-30 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Xiaoming Chen
Publisher : Springer
ISBN 13 : 9811365970
Total Pages : 130 pages
Book Rating : 4.73/5 ( download)

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Download or read book Massive Access for Cellular Internet of Things Theory and Technique written by Xiaoming Chen and published by Springer. This book was released on 2019-05-07 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on massive access to the cellular internet of things (IoT). Both theory and technique are addressed, with more weight placed on the latter. This is achieved by providing in-depth studies on a number of central topics such as channel state information acquisition, user clustering, superposition coding, and successive interference cancellation. Four typical application scenarios are examined in detail, namely the stationary IoT device scenario, frequency division duplex-based low-mobility IoT device scenario, time-division duplex-based IoT device scenario, and high-mobility IoT device scenario. The comprehensive and systematic treatment of key techniques in massive access to the cellular IoT is one of the major features of the book, which is particularly suited for readers who are interested in finding practical solutions for the cellular IoT. As such, it will benefit researchers, engineers, and graduate students in the fields of information engineering, telecommunications engineering, computer engineering, etc.

Author : Satya Prakash Yadav
Publisher : Springer Nature
ISBN 13 : 3030855597
Total Pages : 269 pages
Book Rating : 4.98/5 ( download)

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Download or read book Federated Learning for IoT Applications written by Satya Prakash Yadav and published by Springer Nature. This book was released on 2022-02-02 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Yan Zhang
Publisher : Springer Nature
ISBN 13 : 3030839443
Total Pages : 123 pages
Book Rating : 4.44/5 ( download)

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Download or read book Mobile Edge Computing written by Yan Zhang and published by Springer Nature. This book was released on 2021-10-01 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks.The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management.The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists.