Standard
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.
In:
Foundations and Trends in Machine Learning, Vol. 14, No. 1-2, 2021, p. 1-210.
Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
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, Gascon, 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, Konecny, J, Korolova, A, Koushanfar, F, Koyejo, S, Lepoint, T, Liu, Y, Mittal, P, Mohri, M, Nock, R, Ozgur, A
, Pagh, R, Qi, H, Ramage, D, Raskar, R, Raykova, M, Song, D, Song, W, Stich, SU, Sun, Z, Suresh, AT, Tramer, 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/10.1561/2200000083APA
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., Gascon, 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/10.1561/2200000083Vancouver
Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN et al.
Advances and Open Problems in Federated Learning.
Foundations and Trends in Machine Learning. 2021;14(1-2):1-210.
https://doi.org/10.1561/2200000083Author
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. / Advances and Open Problems in Federated Learning. In: Foundations and Trends in Machine Learning. 2021 ; Vol. 14, No. 1-2. pp. 1-210.
Bibtex
@article{10a0d7b9a25642978f16a573b8a8216d,
title = "Advances and Open Problems in Federated Learning",
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.",
keywords = "DIFFERENTIAL PRIVACY, BIAS, INFERENCE",
author = "Peter Kairouz and McMahan, {H. Brendan} and Brendan Avent and Aurelien Bellet and Mehdi Bennis and Bhagoji, {Arjun Nitin} and Kallista Bonawitz and Zachary Charles and Graham Cormode and Rachel Cummings and D'Oliveira, {Rafael G. L.} and Hubert Eichner and {El Rouayheb}, Salim and David Evans and Josh Gardner and Zachary Garrett and Adria Gascon and Badih Ghazi and Gibbons, {Phillip B.} and Marco Gruteser and Zaid Harchaoui and Chaoyang He and Lie He and Zhouyuan Huo and Ben Hutchinson and Justin Hsu and Martin Jaggi and Tara Javidi and Gauri Joshi and Mikhail Khodak and Jakub Konecny and Aleksandra Korolova and Farinaz Koushanfar and Sanmi Koyejo and Tancrede Lepoint and Yang Liu and Prateek Mittal and Mehryar Mohri and Richard Nock and Ayfer Ozgur and Rasmus Pagh and Hang Qi and Daniel Ramage and Ramesh Raskar and Mariana Raykova and Dawn Song and Weikang Song and Stich, {Sebastian U.} and Ziteng Sun and Suresh, {Ananda Theertha} and Florian Tramer and Praneeth Vepakomma and Jianyu Wang and Li Xiong and Zheng Xu and Qiang Yang and Yu, {Felix X.} and Han Yu and Sen Zhao",
year = "2021",
doi = "10.1561/2200000083",
language = "English",
volume = "14",
pages = "1--210",
journal = "Foundations and Trends in Machine Learning",
issn = "1935-8237",
publisher = "Now Publishers Inc",
number = "1-2",
}
RIS
TY - JOUR
T1 - Advances and Open Problems in Federated Learning
AU - Kairouz, Peter
AU - McMahan, H. Brendan
AU - Avent, Brendan
AU - Bellet, Aurelien
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 - Gascon, Adria
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
AU - Javidi, Tara
AU - Joshi, Gauri
AU - Khodak, Mikhail
AU - Konecny, Jakub
AU - Korolova, Aleksandra
AU - Koushanfar, Farinaz
AU - Koyejo, Sanmi
AU - Lepoint, Tancrede
AU - Liu, Yang
AU - Mittal, Prateek
AU - Mohri, Mehryar
AU - Nock, Richard
AU - Ozgur, 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 - Tramer, 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
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - DIFFERENTIAL PRIVACY
KW - BIAS
KW - INFERENCE
U2 - 10.1561/2200000083
DO - 10.1561/2200000083
M3 - Journal article
VL - 14
SP - 1
EP - 210
JO - Foundations and Trends in Machine Learning
JF - Foundations and Trends in Machine Learning
SN - 1935-8237
IS - 1-2
ER -
