Federated Learning for Future Intelligent Wireless Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Federated Learning for Future Intelligent Wireless Networks write by Yao Sun. This book was released on 2023-12-04. Federated Learning for Future Intelligent Wireless Networks available 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.
Fog Radio Access Networks (F-RAN)
Fog Radio Access Networks (F-RAN) - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Fog Radio Access Networks (F-RAN) write by Mugen Peng. This book was released on 2020-08-12. Fog Radio Access Networks (F-RAN) available 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.
Federated Learning for Wireless Networks
Federated Learning for Wireless Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Federated Learning for Wireless Networks write by Choong Seon Hong. This book was released on 2022-01-01. Federated Learning for Wireless Networks available 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.
Communication Efficient Federated Learning for Wireless Networks
Communication Efficient Federated Learning for Wireless Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Communication Efficient Federated Learning for Wireless Networks write by Mingzhe Chen. This book was released on . Communication Efficient Federated Learning for Wireless Networks available in PDF, EPUB and Kindle.
Federated Learning Over Wireless Edge Networks
Federated Learning Over Wireless Edge Networks - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Federated Learning Over Wireless Edge Networks write by Wei Yang Bryan Lim. This book was released on 2022. Federated Learning Over Wireless Edge Networks available 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. Provides a concise introduction to Federated Learning (FL) and how it enables Edge Intelligence; Highlights the challenges inherent to achieving scalable implementation of FL at the wireless edge; Presents how FL can address challenges resulting from the confluence of AI and wireless communications.
