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:

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.

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.

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 2022-08-04 with total page 559 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover connections between these transformative and impactful technologies, through comprehensive introductions and real-world examples.

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.

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.

Download or read book Advances in Artificial Intelligence and Security written by Xingming Sun and published by Springer Nature. This book was released on 2022-07-08 with total page 732 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 3-volume set CCIS 1586, CCIS 1587 and CCIS 1588 constitutes the refereed proceedings of the 8th International Conference on Artificial Intelligence and Security, ICAIS 2022, which was held in Qinghai, China, in July 2022. The total of 115 full papers and 53 short papers presented in this 3-volume proceedings was carefully reviewed and selected from 1124 submissions. The papers were organized in topical sections as follows: Part I: artificial intelligence; Part II: artificial intelligence; big data; cloud computing and security; multimedia forensics; Part III: encryption and cybersecurity; information hiding; IoT security.

Download or read book 6G Mobile Wireless Networks written by Yulei Wu and published by Springer Nature. This book was released on 2021-08-24 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Computational Modeling and Simulation of Advanced Wireless Communication Systems written by Agbotiname Lucky Imoize and published by CRC Press. This book was released on 2024-08-20 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book covers the exploitation of computational models for effectively developing and managing large-scale wireless communication systems. The goal is to create and establish computational models for seamless human interaction and efficient decision-making in beyond 5G wireless systems. Computational Modeling and Simulation of Advanced Wireless Communication Systems looks to create and establish computational models for seamless human interaction and efficient decision-making in the beyond 5G wireless systems. This book presents the design and development of several computational modeling techniques and their applications in wireless communication systems. It examines shortcomings and limitations of the existing computational models and offers solutions to revamp the traditional architecture toward addressing the vast network issues in wireless systems. The book addresses the need to design efficient computational and simulation models to address several issues in wireless communication systems, such as interference, pathloss, delay, traffic outage, and so forth. It discusses how theoretical, mathematical, and experimental results are integrated for optimal system performance to enhance the quality of service for mobile subscribers. Further, the book is intended for industry and academic researchers, scientists, and engineers in the fields of wireless communications and ICTs. It is structured to present a practical guide to wireless communication engineers, IT practitioners, researchers, students, and other professionals.

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.

Download or read book 2021 International Wireless Communications and Mobile Computing IWCMC written by IEEE Staff and published by . This book was released on 2021-06-28 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: IWCMC 2021 will target a wide spectrum of the state of the art as well as emerging topics pertaining to wireless networks, wireless sensors, vehicular communications, and mobile computing

Download or read book Federated Learning written by Jayakrushna Sahoo and published by CRC Press. This book was released on 2024-09-20 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new book provides an in-depth understanding of federated learning, a new and increasingly popular learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. The volume explores how federated learning integrates AI technologies, such as blockchain, machine learning, IoT, edge computing, and fog computing systems, allowing multiple collaborators to build a robust machine-learning model using a large dataset. It highlights the capabilities and benefits of federated learning, addressing critical issues such as data privacy, data security, data access rights, and access to heterogeneous data. The volume first introduces the general concepts of machine learning and then summarizes the federated learning system setup and its associated terminologies. It also presents a basic classification of FL, the application of FL for various distributed computing scenarios, an integrated view of applications of software-defined networks, etc. The book also explores the role of federated learning in the Internet of Medical Things systems as well. The book provides a pragmatic analysis of strategies for developing a communication-efficient federated learning system. It also details the applicability of blockchain with federated learning on IoT-based systems. It provides an in-depth study of FL-based intrusion detection systems, discussing their taxonomy and functioning and showcasing their superiority over existing systems. The book is unique in that it evaluates the privacy and security aspects in federated learning. The volume presents a comprehensive analysis of some of the common challenges, proven threats, and attack strategies affecting FL systems. Special coverage on protected shot-based federated learning for facial expression recognition is also included. This comprehensive book, Federated Learning: Principles, Paradigms, and Applications, will enable research scholars, information technology professionals, and distributed computing engineers to understand various aspects of federated learning concepts and computational techniques for real-life implementation.

Download or read book Computing Communication and Learning written by Sanjaya Kumar Panda and published by Springer Nature. This book was released on 2023-01-01 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the refereed proceedings of the First International Conference on Computing, Communication and Learning, CoCoLe 2022, held in Warangal, India, in October 2022. The 25 full papers and 1 short paper presented were carefully reviewed and selected from 117 submissions. The CoCoLe conference focuses on three broad areas of computer science and other allied branches, namely computing, communication, and learning.

Download or read book Interconnected Modern Multi Energy Networks and Intelligent Transportation Systems written by Mohammadreza Daneshvar and published by John Wiley & Sons. This book was released on 2024-02-28 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely introduction to the revolutionary technologies reshaping the global energy market The search for more efficient and sustainable ways to meet society’s energy requirements has driven recent technological innovation on an unprecedented scale. The energy needs of a growing population coupled with concerns about climate change have posed unique challenges that necessitate novel energy technologies . The transition of modern energy grids towards multi-energy networks, or MENs, promises to be a fundamental transformation in the way we energize our world. Interconnected Modern Multi-Energy Networks and Intelligent Transportation Systems presents an overview of the foundational methodologies and technologies underlying MENs and the groundbreaking vehicle systems that bring them together. With the inclusion of transformative technologies from radically different sectors, the content covered in this book will be of high value for researchers interested in future energy systems. Readers will also find: In-depth examination of the process of switching from conventional transportation systems to modern intelligent transportation ones Detailed discussions of topics including self-driving vehicles, hybrid energy technologies, grid-edge, and more The introduction of a holistic, reconfigurable system adaptable to vastly different conditions and forms of network interaction Interconnected Modern Multi-Energy Networks and Intelligent Transportation Systems is useful for researchers in electrical, mechanical, civil, architectural, or environmental engineering, as well as for telecommunications researchers and for any industry professionals with an interest in energy transportation.

Download or read book Smart Computing and Communication written by Meikang Qiu and published by Springer Nature. This book was released on 2022-03-14 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 6th International Conference on Smart Computing and Communication, SmartCom 2021, which took place in New York City, USA, during December 29–31, 2021.* The 44 papers included in this book were carefully reviewed and selected from 165 submissions. The scope of SmartCom 2021 was broad, from smart data to smart communications, from smart cloud computing to smart security. The conference gathered all high-quality research/industrial papers related to smart computing and communications and aimed at proposing a reference guideline for further research. * Conference was held online due to the COVID-19 pandemic.

Download or read book Federated Learning written by Heiko Ludwig and published by Springer Nature. This book was released on 2022-07-07 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: Federated Learning: A Comprehensive Overview of Methods and Applications presents an in-depth discussion of the most important issues and approaches to federated learning for researchers and practitioners. Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. Data are retained by data parties that participate in the FL process and are not shared with any other entity. This makes FL an increasingly popular solution for machine learning tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory or practical reasons. This book explains recent progress in research and the state-of-the-art development of Federated Learning (FL), from the initial conception of the field to first applications and commercial use. To obtain this broad and deep overview, leading researchers address the different perspectives of federated learning: the core machine learning perspective, privacy and security, distributed systems, and specific application domains. Readers learn about the challenges faced in each of these areas, how they are interconnected, and how they are solved by state-of-the-art methods. Following an overview on federated learning basics in the introduction, over the following 24 chapters, the reader will dive deeply into various topics. A first part addresses algorithmic questions of solving different machine learning tasks in a federated way, how to train efficiently, at scale, and fairly. Another part focuses on providing clarity on how to select privacy and security solutions in a way that can be tailored to specific use cases, while yet another considers the pragmatics of the systems where the federated learning process will run. The book also covers other important use cases for federated learning such as split learning and vertical federated learning. Finally, the book includes some chapters focusing on applying FL in real-world enterprise settings.

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.