<p>A thorough treatment of UAV wireless communications and networking research challenges and opportunities. Detailed, step-by-step development of carefully selected research problems that pertain to UAV network performance analysis and optimization, physical layer design, trajectory path planning, resource management, multiple access, cooperative communications, standardization, control, and security is provided. Featuring discussion of practical applications including drone delivery systems, public safety, IoT, virtual reality, and smart cities, this is an essential tool for researchers, students, and engineers interested in broadening their knowledge of the deployment and operation of communication systems that integrate or rely on unmanned aerial vehicles.</p>

Wireless Communications and Networking for Unmanned Aerial Vehicles

Science & Math

Saad, Walid

Bennis, Mehdi

Mozaffari, Mohammad

Lin, Xingqin

Astronomy & Space Science

Cambridge University Press

<p>The term Federated Learning was coined as recently as 2016 to describe a machine learning setting where multiple entities collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client's raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective.Since then, the topic has gathered much interest across many different disciplines and the realization that solving many of these interdisciplinary problems likely requires not just machine learning but techniques from distributed optimization, cryptography, security, differential privacy, fairness, compressed sensing, systems, information theory, statistics, and more.This monograph has contributions from leading experts across the disciplines, who describe the latest state-of-the art from their perspective. These contributions have been carefully curated into a comprehensive treatment that enables the reader to understand the work that has been done and get pointers to where effort is required to solve many of the problems before Federated Learning can become a reality in practical systems.Researchers working in the area of distributed systems will find this monograph an enlightening read that may inspire them to work on the many challenging issues that are outlined. This monograph will get the reader up to speed quickly and easily on what is likely to become an increasingly important topic: Federated Learning.</p>

Advances and Open Problems in Federated Learning (Foundations and Trends(r) in Machine Learning)

Computers & Technology

Hutchinson, Ben

Raskar, Ramesh

Mohri, Mehryar

Liu, Yang

Yang, Qiang

Yu, Han

Evans,David

Song, Dawn

Joshi, Gauri

Kairouz, Peter

McMahan, H Brendan

Avent, Brendan

Bellet, Aurélien

Bhagoji, Arjun Nitin

Bonawit, Kallista

Charles, Zachary

Cormode, Graham

Cummings, Rachel

D'Oliveira, Rafael G L

Eichner, Hubert

El Rouayheb, Salim

Gardner, Josh

Garrett, Zachary

Gascón, Adrià

Ghazi, Badih

Gibbons, Phillip B

Gruteser, Marco

Harchaoui, Zaid

He, Chaoyang

He, Lie

Huo, Zhouyuan

Hsu, Justin

Jäggi, Martin

Javidi, Tara

Khodak, Mikhail

Konecný, Jakub

Korolova, Aleksandra

Koushanfar, Farinaz

Koyejo, Sanmi

Lepoint, Tancrède

Mittal, Prateek

Nock, Richard

Özgür, Ayfer

Pagh, Rasmus

Qi, Hang

Ramage, Daniel

Raykova, Mariana

Song, Weikang

Stich, Sebastian U

Sun, Ziteng

Theertha Suresh, Ananda

Tramèr, Florian

Vepakomma, Praneeth

Wang, Jianyu

Xiong, Li

Xu, Zheng

Yu, Felix X

Zhao, Sen

Computer Science

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