Advances and open problems in federated learning

Kairouz, Peter; McMahan, H. Brendan; Avent, Brendan; Bellet, Aurélien; 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; Gascón, Adriá; 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; Özgür, Ayfer; Pagh, Rasmus; Qi, Hang; Ramage, Daniel; Raskar, Ramesh; Raykova, Mariana; Song, Dawn; Song, Weikang; Stich, Sebastian U.; Sun, Ziteng; Theertha Suresh, Ananda; Tramér, Florian; Vepakomma, Praneeth; Wang, Jianyu; Xiong, Li; Xu, Zheng; Yang, Qiang; Yu, Felix X.; Yu, Han; Zhao, Sen (2021-06-23)
 
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URL:
https://doi.org/10.1561/2200000083

Kairouz, Peter
McMahan, H. Brendan
Avent, Brendan
Bellet, Aurélien
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
Gascón, Adriá
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
Özgür, Ayfer
Pagh, Rasmus
Qi, Hang
Ramage, Daniel
Raskar, Ramesh
Raykova, Mariana
Song, Dawn
Song, Weikang
Stich, Sebastian U.
Sun, Ziteng
Theertha Suresh, Ananda
Tramér, Florian
Vepakomma, Praneeth
Wang, Jianyu
Xiong, Li
Xu, Zheng
Yang, Qiang
Yu, Felix X.
Yu, Han
Zhao, Sen
Now Publishers
23.06.2021

Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu and Sen Zhao (2021), "Advances and Open Problems in Federated Learning", Foundations and Trends® in Machine Learning: Vol. 14: No. 1–2, pp 1-210. http://dx.doi.org/10.1561/2200000083

https://rightsstatements.org/vocab/InC/1.0/
© 2021 Peter Kairouz, H. Brendan McMahan, et al. The final publication is available from now publishers via http://dx.doi.org/10.1561/2200000083
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1561/2200000083
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https://urn.fi/URN:NBN:fi-fe2023022828866
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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.

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