Communication-Computation Efficient Federated Learning Over Wireless Network - 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-Computation Efficient Federated Learning Over Wireless Network write by Afsaneh Mahmoudi. This book was released on 2023. Communication-Computation Efficient Federated Learning Over Wireless Network available in PDF, EPUB and Kindle.
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 2022-09-28. 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.
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.
Federated Learning for IoT Applications
Federated Learning for IoT Applications - 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 IoT Applications write by Satya Prakash Yadav. This book was released on 2022-02-02. Federated Learning for IoT Applications available 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.
