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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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