Federated Learning for Wireless Networks

Federated Learning for Wireless Networks
Author: Choong Seon Hong
Publisher: Springer Nature
Total Pages: 257
Release: 2022-01-01
Genre: Computers
ISBN: 9811649634
GET BOOK

Download Federated Learning for Wireless Networks Book in PDF, Epub and Kindle

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.

Federated Learning for Future Intelligent Wireless Networks

Federated Learning for Future Intelligent Wireless Networks
Author: Yao Sun
Publisher: John Wiley & Sons
Total Pages: 324
Release: 2023-12-04
Genre: Technology & Engineering
ISBN: 1119913918
GET BOOK

Download Federated Learning for Future Intelligent Wireless Networks Book in PDF, Epub and Kindle

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.

Machine Learning and Wireless Communications

Machine Learning and Wireless Communications
Author: Yonina C. Eldar
Publisher: Cambridge University Press
Total Pages: 560
Release: 2022-06-30
Genre: Technology & Engineering
ISBN: 1108967736
GET BOOK

Download Machine Learning and Wireless Communications Book in PDF, Epub and Kindle

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.

Federated Learning Over Wireless Edge Networks

Federated Learning Over Wireless Edge Networks
Author: Wei Yang Bryan Lim
Publisher: Springer Nature
Total Pages: 175
Release: 2022-09-28
Genre: Technology & Engineering
ISBN: 3031078381
GET BOOK

Download Federated Learning Over Wireless Edge Networks Book in PDF, Epub and Kindle

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.

6G Mobile Wireless Networks

6G Mobile Wireless Networks
Author: Yulei Wu
Publisher: Springer Nature
Total Pages: 472
Release: 2021-08-24
Genre: Computers
ISBN: 3030727777
GET BOOK

Download 6G Mobile Wireless Networks Book in PDF, Epub and Kindle

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

Federated Learning for IoT Applications
Author: Satya Prakash Yadav
Publisher: Springer Nature
Total Pages: 269
Release: 2022-02-02
Genre: Technology & Engineering
ISBN: 3030855597
GET BOOK

Download Federated Learning for IoT Applications Book in PDF, Epub and Kindle

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.

Fog Radio Access Networks (F-RAN)

Fog Radio Access Networks (F-RAN)
Author: Mugen Peng
Publisher: Springer Nature
Total Pages: 227
Release: 2020-08-12
Genre: Technology & Engineering
ISBN: 3030507351
GET BOOK

Download Fog Radio Access Networks (F-RAN) Book in PDF, Epub and Kindle

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.

Wireless Network Intrusion Detection and Analysis Using Federated Learning

Wireless Network Intrusion Detection and Analysis Using Federated Learning
Author: Burak Cetin
Publisher:
Total Pages: 34
Release: 2020
Genre: Intrusion detection systems (Computer security)
ISBN:
GET BOOK

Download Wireless Network Intrusion Detection and Analysis Using Federated Learning Book in PDF, Epub and Kindle

Wi-Fi has become the wireless networking standard that allows short-to medium-range devices to connect without wires. For the last 20 years, the Wi-Fi technology has been so pervasive that most devices in use today are mobile and connect to the internet through Wi-Fi. Unlike wired network, a wireless network lacks a clear boundary, which leads to significant Wi-Fi network security concerns, especially because the current security measures are prone to several types of intrusion. To address this problem, machine learning and deep learning methods have been successfully developed to identify network attacks. However, collecting data to develop models is expensive and raises privacy concerns. The goal of this thesis is to evaluate a federated learning approach that would alleviate such privacy concerns. This work on intrusion detection is performed in a simulated environment. During the work, different experiments have concluded to define points that can affect the accuracy of a model to allow edge devices to collaboratively update global anomaly detection models using a privacy-aware approach. Three comparison tests were done in order to find the optimal results; different training rates, different training methods, different parameters. Using different combinations of 5 parameters - training algorithms, number of epochs, devices per round, round numbers and size of the sample set-, these tests with the AWID intrusion detection data set, show that our federated approach is effective in terms of classification accuracy (with an accuracy range of 88-95%), computation cost, as well as communication cost. In our study, the best case had the most rounds, epoch and the devices per round compared to the others.

Federated Learning

Federated Learning
Author: Qiang Yang
Publisher: Springer Nature
Total Pages: 291
Release: 2020-11-25
Genre: Computers
ISBN: 3030630765
GET BOOK

Download Federated Learning Book in PDF, Epub and Kindle

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.”