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Advances and Open Problems in Federated Learning

Peter Kairouz
H. Brendan McMahan
Brendan Avent
Aurélien Bellet
Mehdi Bennis
Arjun Nitin Bhagoji
K. A. Bonawitz
Zachary Charles
Graham Cormode
Rachel Cummings
Rafael G.L. D'Oliveira
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 Konečný
Aleksandra Korolova
Farinaz Koushanfar
Sanmi Koyejo
Tancrède Lepoint
Yang Liu
Prateek Mittal
Mehryar Mohri
Richard Nock
Ayfer Özgür
Rasmus Pagh
Mariana Raykova
Hang Qi
Daniel Ramage
Ramesh Raskar
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
Arxiv (2019)
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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 mitigates 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 a comprehensive list of open problems and challenges.

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