University of Oulu

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.

Advances and open problems in federated learning

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Author: Kairouz, Peter1; McMahan, H. Brendan1; Avent, Brendan2;
Organizations: 1Google Research
2University of Southern California
4University of Oulu
5Princeton University
6University of Warwick
7Georgia Institute of Technology
8Rutgers University
9University of Virginia
10University of Washington
11Carnegie Mellon University
12Ecole Polytechnique Fédérale de Lausanne
13University of Pittsburgh
14University of Wisconsin–Madison
15University of California San Diego
16University of Illinois Urbana-Champaign
17Nanyang Technological University
18Australian National University
19Stanford University
20IT University of Copenhagen
21Massachusetts Institute of Technology
22University of California Berkeley
23Cornell University
24Emory University
25Hong Kong University of Science and Technology
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.9 MB)
Persistent link:
Language: English
Published: Now Publishers, 2021
Publish Date: 2023-02-28


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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Series: Foundations and trends in machine learning
ISSN: 1935-8237
ISSN-E: 1935-8245
ISSN-L: 1935-8237
Volume: 14
Issue: 1-2
Pages: 1 - 210
DOI: 10.1561/2200000083
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Copyright information: © 2021 Peter Kairouz, H. Brendan McMahan, et al. The final publication is available from now publishers via