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Federated Learning Over Wireless Edge Networks
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Book Synopsis Federated Learning Over Wireless Edge Networks by : Wei Yang Bryan Lim
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 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.
Book Synopsis Federated Learning for Wireless Networks by : Choong Seon Hong
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 Federated Learning written by Qiang Yang and published by Springer Nature. This book was released on 2020-11-25 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
Book Synopsis 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) by : IEEE Staff
Download or read book 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) written by IEEE Staff and published by . This book was released on 2020-05-26 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The conference focus on the general area of signal processing advances in wireless communications It will include, but not limited to, the following topics Coding, Modulation, and Equalization Detection, Estimation and Filtering in Wireless Communications Beamforming, Precoding and Transceiver Designs of MIMO Massive and millimeter MIMO systems Full duplex Communications Energy Efficiency and Energy Harvesting Network Design and Performance Optimization Cooperative Communications and Multiuser Networks Resource Allocation and Scheduling in Wireless Networks Internet of Things and Connected Vehicles Distributed Signal Processing Localization and Tracking in Wireless Networks Machine Learning for Wireless Communications and Networking Future Trends and Emerging Technologies
Book Synopsis Machine Learning and Wireless Communications by : Yonina C. Eldar
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.
Book Synopsis 6G Mobile Wireless Networks by : Yulei Wu
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.
Book Synopsis Service-Oriented Computing by : Eleanna Kafeza
Download or read book Service-Oriented Computing written by Eleanna Kafeza and published by Springer Nature. This book was released on 2020-12-08 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 18th International Conference on Service-Oriented Computing, ICSOC 2020, which was planned to take place in Dubai, UAE, during December 14-17, 2020. Due to the COVID-19 pandemic the conference was held online. The 23 full, 16 short, and 3 industry papers included in this volume were carefully reviewed and selected from 137 submissions. They were organized in topical sections named: microservices; Internet of Things; services at the edge; machine learning for service oriented computing; smart data and smart services; service oriented technology trends; industry papers.
Book Synopsis Communication Efficient Federated Learning for Wireless Networks by : Mingzhe Chen
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:
Book Synopsis Mobile Edge Computing by : Yan Zhang
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.
Download or read book Age of Information written by Yin Sun and published by Morgan & Claypool Publishers. This book was released on 2019-12-12 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information usually has the highest value when it is fresh. For example, real-time knowledge about the location, orientation, and speed of motor vehicles is imperative in autonomous driving, and the access to timely information about stock prices and interest rate movements is essential for developing trading strategies on the stock market. The Age of Information (AoI) concept, together with its recent extensions, provides a means of quantifying the freshness of information and an opportunity to improve the performance of real-time systems and networks. Recent research advances on AoI suggest that many well-known design principles of traditional data networks (for, e.g., providing high throughput and low delay) need to be re-examined for enhancing information freshness in rapidly emerging real-time applications. This book provides a suite of analytical tools and insightful results on the generation of information-update packets at the source nodes and the design of network protocols forwarding the packets to their destinations. The book also points out interesting connections between AoI concept and information theory, signal processing, and control theory, which are worthy of future investigation.
Download or read book Edge AI written by Xiaofei Wang and published by Springer Nature. This book was released on 2020-08-31 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: As an important enabler for changing people’s lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e) using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing.
Book Synopsis Federated Learning by : Jayakrushna Sahoo
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.
Book Synopsis Fog Radio Access Networks (F-RAN) by : Mugen Peng
Download or read book Fog Radio Access Networks (F-RAN) written by Mugen Peng and published by Springer Nature. This book was released on 2020-08-12 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive introduction of Fog Radio Access Networks (F-RANs), from both academic and industry perspectives. The authors first introduce the network architecture and the frameworks of network management and resource allocation for F-RANs. They then discuss the recent academic research achievements of F-RANs, such as the analytical results of theoretical performance limits and optimization theory-based resource allocation techniques. Meanwhile, they discuss the application and implementations of F-RANs, including the latest standardization procedure, and the prototype and test bed design. The book is concluded by summarizing the existing open issues and future trends of F-RANs. Includes the latest theoretical and technological research achievements of F-RANs, also discussing existing open issues and future trends of F-RANs toward 6G from an interdisciplinary perspective; Provides commonly-used tools for research and development of F-RANs such as open resource projects for implementing prototypes and test beds; Includes examples of prototype and test bed design and gives tools to evaluate the performance of F-RANs in simulations and experimental circumstances.
Book Synopsis Mobile Edge Artificial Intelligence by : Yuanming Shi
Download or read book Mobile Edge Artificial Intelligence written by Yuanming Shi and published by Academic Press. This book was released on 2021-08-07 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mobile Edge Artificial Intelligence: Opportunities and Challenges presents recent advances in wireless technologies and nonconvex optimization techniques for designing efficient edge AI systems. The book includes comprehensive coverage on modeling, algorithm design and theoretical analysis. Through typical examples, the powerfulness of this set of systems and algorithms is demonstrated, along with their abilities to make low-latency, reliable and private intelligent decisions at network edge. With the availability of massive datasets, high performance computing platforms, sophisticated algorithms and software toolkits, AI has achieved remarkable success in many application domains. As such, intelligent wireless networks will be designed to leverage advanced wireless communications and mobile computing technologies to support AI-enabled applications at various edge mobile devices with limited communication, computation, hardware and energy resources. - Presents advanced key enabling techniques, including model compression, wireless MapReduce and wireless cooperative transmission - Provides advanced 6G wireless techniques, including over-the-air computation and reconfigurable intelligent surface - Includes principles for designing communication-efficient edge inference systems, communication-efficient training systems, and communication-efficient optimization algorithms for edge machine learning
Book Synopsis Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks by : Krishna Kant Singh
Download or read book Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks written by Krishna Kant Singh and published by John Wiley & Sons. This book was released on 2020-07-08 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.
Book Synopsis Federated Learning by : Heiko Ludwig
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.
