Advances and Open Problems in Federated Learning
Kairouz, Peter; McMahan, H. Brendan; Avent, Brendan; Bellet, Aurelien; Bennis, Mehdi; Bhagoji, Arjun Nitin; Bonawitz, Kallista; Charles, Zachary; Cormode, Graham; Cummings, Rachel; D'Oliveira, Rafael G. L.; Eichner, Hubert; El Rouayheb, Salim; Evans, David; Gardner, Josh; Garrett, Zachary; Gascon, Adria; 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; Konecny, Jakub; Korolova, Aleksandra; Koushanfar, Farinaz; Koyejo, Sanmi; Lepoint, Tancrede; Liu, Yang; Mittal, Prateek; Mohri, Mehryar; Nock, Richard; Ozgur, Ayfer; Pagh, Rasmus; Qi, Hang; Ramage, Daniel; Raskar, Ramesh; Raykova, Mariana; Song, Dawn; Song, Weikang; Stich, Sebastian U.; Sun, Ziteng; Suresh, Ananda Theertha; Tramer, Florian; Vepakomma, Praneeth; Wang, Jianyu; Xiong, Li; Xu, Zheng; Yang, Qiang; Yu, Felix X.; Yu, Han; Zhao, Sen
2021
Abstract
Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this monograph discusses recent advances and presents an extensive collection of open problems and challenges.
Details
Title
Advances and Open Problems in Federated Learning
Author(s)
Kairouz, Peter ; McMahan, H. Brendan ; Avent, Brendan ; Bellet, Aurelien ; Bennis, Mehdi ; Bhagoji, Arjun Nitin ; Bonawitz, Kallista ; Charles, Zachary ; Cormode, Graham ; Cummings, Rachel ; D'Oliveira, Rafael G. L. ; Eichner, Hubert ; El Rouayheb, Salim ; Evans, David ; Gardner, Josh ; Garrett, Zachary ; Gascon, Adria ; 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 ; Konecny, Jakub ; Korolova, Aleksandra ; Koushanfar, Farinaz ; Koyejo, Sanmi ; Lepoint, Tancrede ; Liu, Yang ; Mittal, Prateek ; Mohri, Mehryar ; Nock, Richard ; Ozgur, Ayfer ; Pagh, Rasmus ; Qi, Hang ; Ramage, Daniel ; Raskar, Ramesh ; Raykova, Mariana ; Song, Dawn ; Song, Weikang ; Stich, Sebastian U. ; Sun, Ziteng ; Suresh, Ananda Theertha ; Tramer, Florian ; Vepakomma, Praneeth ; Wang, Jianyu ; Xiong, Li ; Xu, Zheng ; Yang, Qiang ; Yu, Felix X. ; Yu, Han ; Zhao, Sen
Published in
Foundations And Trends In Machine Learning
Volume
14
Issue
1-2
Pages
1-210
Date
2021-01-01
Publisher
Hanover, NOW PUBLISHERS INC
ISSN
1935-8237
1935-8245
1935-8245
Keywords
Other identifier(s)
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Laboratories
MLO
Record Appears in
Scientific production and competences > I&C - School of Computer and Communication Sciences > IINFCOM > MLO - Machine Learning and Optimization Laboratory
Peer-reviewed publications
Work produced at EPFL
Journal Articles
Published
Peer-reviewed publications
Work produced at EPFL
Journal Articles
Published
Record creation date
2021-07-17