Advances and Open Problems in Federated Learning

Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. 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, Zaïd 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, Sen Zhao

FnTML 2021 pp. 1-210

doi:10.1561/2200000083 /ftml/2021/kairouz2021ftml-advances/

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 paper discusses recent advances and presents an extensive collection of open problems and challenges.

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Cite

Text

Kairouz et al. "Advances and Open Problems in Federated Learning." Foundations and Trends in Machine Learning, 2021. doi:10.1561/2200000083

Markdown

[Kairouz et al. "Advances and Open Problems in Federated Learning." Foundations and Trends in Machine Learning, 2021.](https://mlanthology.org/ftml/2021/kairouz2021ftml-advances/) doi:10.1561/2200000083

BibTeX

@article{kairouz2021ftml-advances,
  title     = {{Advances and Open Problems in Federated Learning}},
  author    = {Kairouz, Peter and McMahan, H. Brendan and Avent, Brendan and Bellet, Aurélien and Bennis, Mehdi and Bhagoji, Arjun Nitin and Bonawitz, Kallista A. and Charles, Zachary and Cormode, Graham and Cummings, Rachel and D'Oliveira, Rafael G. L. and Eichner, Hubert and El Rouayheb, Salim and Evans, David and Gardner, Josh and Garrett, Zachary and Gascón, Adrià and Ghazi, Badih and Gibbons, Phillip B. and Gruteser, Marco and Harchaoui, Zaïd and He, Chaoyang and He, Lie and Huo, Zhouyuan and Hutchinson, Ben and Hsu, Justin and Jaggi, Martin and Javidi, Tara and Joshi, Gauri and Khodak, Mikhail and Konecný, Jakub and Korolova, Aleksandra and Koushanfar, Farinaz and Koyejo, Sanmi and Lepoint, Tancrède and Liu, Yang and Mittal, Prateek and Mohri, Mehryar and Nock, Richard and Özgür, Ayfer and Pagh, Rasmus and Qi, Hang and Ramage, Daniel and Raskar, Ramesh and Raykova, Mariana and Song, Dawn and Song, Weikang and Stich, Sebastian U. and Sun, Ziteng and Suresh, Ananda Theertha and Tramèr, Florian and Vepakomma, Praneeth and Wang, Jianyu and Xiong, Li and Xu, Zheng and Yang, Qiang and Yu, Felix X. and Yu, Han and Zhao, Sen},
  journal   = {Foundations and Trends in Machine Learning},
  year      = {2021},
  pages     = {1-210},
  doi       = {10.1561/2200000083},
  volume    = {14},
  url       = {https://mlanthology.org/ftml/2021/kairouz2021ftml-advances/}
}