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Advances and Open Problems in Federated Learning

Peter Kairouz 1 H. Brendan Mcmahan 1 Brendan Avent 2 Aurélien Bellet 3 Mehdi Bennis 4 Arjun Nitin Bhagoji 5 Keith Bonawitz 1 Zachary Charles 1 Graham Cormode 6 Rachel Cummings 7 Rafael G. L. d'Oliveira 8 Salim El Rouayheb 8 David Evans 9 Josh Gardner 10 Zachary Garrett 1 Adrià Gascón 1 Badih Ghazi 1 Phillip B. Gibbons 11 Marco Gruteser 1, 8 Zaid Harchaoui 10 Chaoyang He 2 Lie He 12 Zhouyuan Huo 13 Ben Hutchinson 1 Justin Hsu 14 Martin Jaggi 12 Tara Javidi 15 Gauri Joshi 11 Mikhail Khodak 11 Jakub Konečný 1 Aleksandra Korolova 2 Farinaz Koushanfar 15 Sanmi Koyejo 1, 16 Tancrède Lepoint 1 Yang Liu 17 Prateek Mittal 5 Mehryar Mohri 1 Richard Nock 18 Ayfer Ozgür 19 Rasmus Pagh 1, 20 Mariana Raykova 1 Hang Qi 1 Daniel Ramage 1 Ramesh Raskar 21 Dawn Song 22 Weikang Song 1 Sebastian U. Stich 12 Ziteng Sun 23 Ananda Theertha Suresh 1 Florian Tramèr 19 Praneeth Vepakomma 21 Jianyu Wang 11 Li Xiong 24 Zheng Xu 1 Qiang Yang 25 Felix X. Yu 1 Han Yu 17 Sen Zhao 1
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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https://hal.inria.fr/hal-02406503
Contributor : Aurélien Bellet <>
Submitted on : Thursday, December 12, 2019 - 10:37:09 AM
Last modification on : Friday, February 7, 2020 - 5:24:02 PM
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  • HAL Id : hal-02406503, version 1
  • ARXIV : 1912.04977

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Peter Kairouz, H. Brendan Mcmahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, et al.. Advances and Open Problems in Federated Learning. 2019. ⟨hal-02406503⟩

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