Communication Efficient Federated Learning for Wireless Networks
Author: Mingzhe Chen
Publisher: Springer Nature
Published:
Total Pages: 189
ISBN-13: 3031512669
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Federated Learning for Wireless Networks
Author: Choong Seon Hong
Publisher: Springer Nature
Published: 2022-01-01
Total Pages: 257
ISBN-13: 9811649634
DOWNLOAD EBOOKRecently 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 and Wireless Communications
Author: Yonina C. Eldar
Publisher: Cambridge University Press
Published: 2022-06-30
Total Pages: 560
ISBN-13: 1108967736
DOWNLOAD EBOOKHow 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.
Machine Learning and Wireless Communications
Author: Yonina C. Eldar
Publisher: Cambridge University Press
Published: 2022-08-04
Total Pages: 559
ISBN-13: 1108832989
DOWNLOAD EBOOKDiscover connections between these transformative and impactful technologies, through comprehensive introductions and real-world examples.
Federated Learning
Author: Qiang Yang
Publisher: Springer Nature
Published: 2020-11-25
Total Pages: 291
ISBN-13: 3030630765
DOWNLOAD EBOOKThis 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.”
Advances in Artificial Intelligence and Security
Author: Xingming Sun
Publisher: Springer Nature
Published: 2022-07-08
Total Pages: 732
ISBN-13: 3031067614
DOWNLOAD EBOOKThe 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.
Federated Learning for Future Intelligent Wireless Networks
Author: Yao Sun
Publisher: John Wiley & Sons
Published: 2023-12-04
Total Pages: 324
ISBN-13: 1119913918
DOWNLOAD EBOOKFederated 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
Author: Jayakrushna Sahoo
Publisher: CRC Press
Published: 2024-09-20
Total Pages: 353
ISBN-13: 1040088597
DOWNLOAD EBOOKThis 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.
Computing, Communication and Learning
Author: Sanjaya Kumar Panda
Publisher: Springer Nature
Published: 2023-01-01
Total Pages: 323
ISBN-13: 3031217500
DOWNLOAD EBOOKThis 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.
Interconnected Modern Multi-Energy Networks and Intelligent Transportation Systems
Author: Mohammadreza Daneshvar
Publisher: John Wiley & Sons
Published: 2024-02-28
Total Pages: 484
ISBN-13: 1394188757
DOWNLOAD EBOOKA 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.
