05152nam a22005175i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050002000172072001600192072002300208072001500231100008300246245012600329250001800455264006900473300006400542336002600606337002600632338003600658347002400694490003400718505046200752520277201214650003203986650002904018650004304047650004104090650004404131650002904175650004604204700008304250710003404333773002604367776003604393776003604429776003604465830004104501856004604542912001404588912001504602999001704617978-3-031-51266-7DE-He21320251030111431.0cr nn 008mamaa240219s2024    sz |    s    |||| 0|eng d  a97830315126679978-3-031-51266-77 a10.1007/978-3-031-51266-72doi 4aTK5105.5-5105.9 7aUKN2bicssc 7aCOM0430002bisacsh 7aUKN2thema1 aChen, Mingzhe.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut97126310aCommunication Efficient Federated Learning for Wireless Networksh[electronic resource] /cby Mingzhe Chen, Shuguang Cui.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXI, 179 p. 46 illus., 44 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aWireless Networks,x2366-14450 aPart. I. Fundamentals and Preliminaries of Federated Learning -- Chapter. 1. Introduction -- Chapter. 2. Fundamentals and Preliminaries of Federated Learning -- Chapter. 3. Resource Management for Federated Learning -- Chapter. 4. Quantization for Federated Learning -- Chapter. 5. Federated Learning with Over the Air Computation -- Chapter. 6. Federated Learning for Autonomous Vehicles Control -- Chapter. 7. Federated Learning for Mobile Edge Computing.  aThis book provides a comprehensive study of Federated Learning (FL) over wireless networks. It consists of three main parts: (a) Fundamentals and preliminaries of FL, (b) analysis and optimization of FL over wireless networks, and (c) applications of wireless FL for Internet-of-Things systems. In particular, in the first part, the authors provide a detailed overview on widely-studied FL framework. In the second part of this book, the authors comprehensively discuss three key wireless techniques including wireless resource management, quantization, and over-the-air computation to support the deployment of FL over realistic wireless networks. It also presents several solutions based on optimization theory, graph theory and machine learning to optimize the performance of FL over wireless networks. In the third part of this book, the authors introduce the use of wireless FL algorithms for autonomous vehicle control and mobile edge computing optimization. Machine learning and data-driven approaches have recently received considerable attention as key enablers for next-generation intelligent networks. Currently, most existing learning solutions for wireless networks rely on centralizing the training and inference processes by uploading data generated at edge devices to data centers. However, such a centralized paradigm may lead to privacy leakage, violate the latency constraints of mobile applications, or may be infeasible due to limited bandwidth or power constraints of edge devices. To address these issues, distributing machine learning at the network edge provides a promising solution, where edge devices collaboratively train a shared model using real-time generated mobile data. The avoidance of data uploading to a central server not only helps preserve privacy but also reduces network traffic congestion as well as communication cost. Federated learning (FL) is one of most important distributed learning algorithms. In particular, FL enables devices to train a shared machine learning model while keeping data locally. However, in FL, training machine learning models requires communication between wireless devices and edge servers over wireless links. Therefore, wireless impairments such as noise, interference, and uncertainties among wireless channel states will significantly affect the training process and performance of FL. For example, transmission delay can significantly impact the convergence time of FL algorithms. In consequence, it is necessary to optimize wireless network performance for the implementation of FL algorithms. This book targets researchers and advanced level students in computer science and electrical engineering. Professionals working in signal processing and machine learning will also buy this book. 0aComputer networks .960157 0aMachine learning.955059 0aWireless communication systems.965781 0aMobile communication systems.96578214aComputer Communication Networks.96568924aMachine Learning.95505924aWireless and Mobile Communication.9657861 aCui, Shuguang.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9712642 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303151265008iPrinted edition:z978303151267408iPrinted edition:z9783031512681 0aWireless Networks,x2366-144596749740uhttps://doi.org/10.1007/978-3-031-51266-7  aZDB-2-SCS  aZDB-2-SXCS  c57707d57707