Advances and open problems in federated learning

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 Konecní; 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; Sen Zhao

doi:10.1561/2200000083

Advances and open problems in federated learning

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 GruteserZaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecní, 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, Sen Zhao

Computer Science

Research output: Contribution to journal › Review article › peer-review

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.

Original language	English (US)
Pages (from-to)	1-210
Number of pages	210
Journal	Foundations and Trends in Machine Learning
Volume	14
Issue number	1-2
DOIs	https://doi.org/10.1561/2200000083
State	Published - Jun 23 2021

ASJC Scopus subject areas

Software
Human-Computer Interaction
Artificial Intelligence

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Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D'Oliveira, R. G. L., Eichner, H., El Rouayheb, S., Evans, D., Gardner, J., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B., ... Zhao, S. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1-2), 1-210. https://doi.org/10.1561/2200000083

Kairouz, P, McMahan, HB, Avent, B, Bellet, A, Bennis, M, Bhagoji, AN, Bonawitz, K, Charles, Z, Cormode, G, Cummings, R, D'Oliveira, RGL, Eichner, H, El Rouayheb, S, Evans, D, Gardner, J, Garrett, Z, Gascón, A, Ghazi, B, Gibbons, PB, Gruteser, M, Harchaoui, Z, He, C, He, L, Huo, Z, Hutchinson, B, Hsu, J, Jaggi, M, Javidi, T, Joshi, G, Khodak, M, Konecní, J, Korolova, A, Koushanfar, F, Koyejo, S, Lepoint, T, Liu, Y, Mittal, P, Mohri, M, Nock, R, Özgür, A, Pagh, R, Qi, H, Ramage, D, Raskar, R, Raykova, M, Song, D, Song, W, Stich, SU, Sun, Z, Suresh, AT, Tramèr, F, Vepakomma, P, Wang, J, Xiong, L, Xu, Z, Yang, Q, Yu, FX, Yu, H & Zhao, S 2021, 'Advances and open problems in federated learning', Foundations and Trends in Machine Learning, vol. 14, no. 1-2, pp. 1-210. https://doi.org/10.1561/2200000083

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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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AU - Avent, Brendan

AU - Bellet, Aurélien

AU - Bennis, Mehdi

AU - Bhagoji, Arjun Nitin

AU - Bonawitz, Kallista

AU - Charles, Zachary

AU - Cormode, Graham

AU - Cummings, Rachel

AU - D'Oliveira, Rafael G.L.

AU - Eichner, Hubert

AU - El Rouayheb, Salim

AU - Evans, David

AU - Gardner, Josh

AU - Garrett, Zachary

AU - Gascón, Adrià

AU - Ghazi, Badih

AU - Gibbons, Phillip B.

AU - Gruteser, Marco

AU - Harchaoui, Zaid

AU - He, Chaoyang

AU - He, Lie

AU - Huo, Zhouyuan

AU - Hutchinson, Ben

AU - Hsu, Justin

AU - Jaggi, Martin

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AU - Konecní, Jakub

AU - Korolova, Aleksandra

AU - Koushanfar, Farinaz

AU - Koyejo, Sanmi

AU - Lepoint, Tancrède

AU - Liu, Yang

AU - Mittal, Prateek

AU - Mohri, Mehryar

AU - Nock, Richard

AU - Özgür, Ayfer

AU - Pagh, Rasmus

AU - Qi, Hang

AU - Ramage, Daniel

AU - Raskar, Ramesh

AU - Raykova, Mariana

AU - Song, Dawn

AU - Song, Weikang

AU - Stich, Sebastian U.

AU - Sun, Ziteng

AU - Suresh, Ananda Theertha

AU - Tramèr, Florian

AU - Vepakomma, Praneeth

AU - Wang, Jianyu

AU - Xiong, Li

AU - Xu, Zheng

AU - Yang, Qiang

AU - Yu, Felix X.

AU - Yu, Han

AU - Zhao, Sen

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JF - Foundations and Trends in Machine Learning

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ER -

Advances and open problems in federated learning

Abstract

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