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

Kairouz, Peter; McMahan, H Brendan; Avent, Brendan; Bellet, Aurélien; Bennis, Mehdi; Bhagoji, Arjun Nitin; Bonawit, Kallista; Charles, Zachary; Cormode, Graham; Cummings, Rachel; D'Oliveira, Rafael G L; Eichner, Hubert; El Rouayheb, Salim; Evans, David; Gardner, Josh; Garrett, Zachary; Gascón, Adrià; Ghazi, Badih; Gibbons, Phillip B; Gruteser, Marco; Harchaoui, Zaid; He, Chaoyang; He, Lie; Huo, Zhouyuan; Hutchinson, Ben; Hsu, Justin; Jaggi, Martin; Javidi, Tara; Joshi, Gauri; Khodak, Mikhail; Konecný, Jakub; Korolova, Aleksandra; Koushanfar, Farinaz; Koyejo, Sanmi; Lepoint, Tancrède; Liu, Yang; Mittal, Prateek; Mohri, Mehryar; Nock, Richard; Özgür, Ayfer; Pagh, Rasmus; Qi, Hang; Ramage, Daniel; Raskar, Ramesh; Raykova, Mariana; Song, Dawn; Song, Weikang; Stich, Sebastian U; Sun, Ziteng; Theertha Suresh, Ananda; Tramèr, Florian; Vepakomma, Praneeth; Wang, Jianyu; Xiong, Li; Xu, Zheng; Yang, Qiang; Yu, Felix X; Yu, Han; Zhao, Sen

ISBN 10: 1680837885 / ISBN 13: 9781680837889

Verlag: Now Publishers, 2021

Beschreibung

In.

Bestandsnummer des Verkäufers ria9781680837889_new

Bibliografische Details

• Titel: Advances and Open Problems in Federated Learning (Foundations and Trends(r) in Machine Learning)
• Verlag: Now Publishers
• Erscheinungsjahr: 2021
• Sprache: Englisch
• ISBN-10: 1680837885
• ISBN-13: 9781680837889
• Einband: Softcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

Charlotte y Peter Fiell son dos autoridades en historia, teoría y crítica del diseño y han escrito más de sesenta libros sobre la materia, muchos de los cuales se han convertido en éxitos de ventas. También han impartido conferencias y cursos como profesores invitados, han comisariado exposiciones y asesorado a fabricantes, museos, salas de subastas y grandes coleccionistas privados de todo el mundo. Los Fiell han escrito numerosos libros para TASCHEN, entre los que se incluyen 1000 Chairs, Diseño del siglo XX, El diseño industrial de la A a la Z, Scandinavian Design y Diseño del siglo XXI.