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Advances and Open Problems in Federated Learning

Dec 10, 2019
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith 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


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Generative Models for Effective ML on Private, Decentralized Datasets

Nov 15, 2019
Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, Blaise Aguera y Arcas

* 27 pages, 8 figures 

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Context-Aware Local Differential Privacy

Oct 31, 2019
Jayadev Acharya, Keith Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun


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Federated Evaluation of On-device Personalization

Oct 22, 2019
Kangkang Wang, Rajiv Mathews, Chloé Kiddon, Hubert Eichner, Françoise Beaufays, Daniel Ramage

* 4 pages, 4 figures 

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Towards Federated Learning at Scale: System Design

Mar 22, 2019
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečný, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander


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Applied Federated Learning: Improving Google Keyboard Query Suggestions

Dec 07, 2018
Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, Françoise Beaufays


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Federated Learning for Mobile Keyboard Prediction

Nov 08, 2018
Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, Daniel Ramage

* 7 pages, 4 figures 

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Learning Differentially Private Recurrent Language Models

Feb 24, 2018
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, Li Zhang

* Camera-ready ICLR 2018 version, minor edits from previous 

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Communication-Efficient Learning of Deep Networks from Decentralized Data

Feb 28, 2017
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas

* Proceedings of the 20 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017. JMLR: W&CP volume 54 
* This version updates the large-scale LSTM experiments, along with other minor changes. In earlier versions, an inconsistency in our implementation of FedSGD caused us to report much lower learning rates for the large-scale LSTM. We reran these experiments, and also found that fewer local epochs offers better performance, leading to slightly better results for FedAvg than previously reported 

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Practical Secure Aggregation for Federated Learning on User-Held Data

Nov 14, 2016
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, Karn Seth

* 5 pages, 1 figure. To appear at the NIPS 2016 workshop on Private Multi-Party Machine Learning 

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Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Oct 08, 2016
Jakub Konečný, H. Brendan McMahan, Daniel Ramage, Peter Richtárik

* 38 pages 

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Discrete Distribution Estimation under Local Privacy

Jun 15, 2016
Peter Kairouz, Keith Bonawitz, Daniel Ramage

* 23 pages, 12 figures, submitted to ICML 2016 (under review) 

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Federated Optimization:Distributed Optimization Beyond the Datacenter

Nov 11, 2015
Jakub Konečný, Brendan McMahan, Daniel Ramage

* NIPS workshop version 

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