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On the Outsized Importance of Learning Rates in Local Update Methods

Jul 02, 2020
Zachary Charles, Jakub Konečný


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Adaptive Federated Optimization

Feb 29, 2020
Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, H. Brendan McMahan


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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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Federated Learning with Autotuned Communication-Efficient Secure Aggregation

Nov 30, 2019
Keith Bonawitz, Fariborz Salehi, Jakub Konečný, Brendan McMahan, Marco Gruteser

* 5 pages, 3 figures. To appear at the IEEE Asilomar Conference on Signals, Systems, and Computers 2019 

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Improving Federated Learning Personalization via Model Agnostic Meta Learning

Sep 27, 2019
Yihan Jiang, Jakub Konečný, Keith Rush, Sreeram Kannan


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SysML: The New Frontier of Machine Learning Systems

May 01, 2019
Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar


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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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A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion

Jan 27, 2019
Filip Hanzely, Jakub Konečný, Nicolas Loizou, Peter Richtárik, Dmitry Grishchenko

* NeurIPS 2018, Privacy Preserving Machine Learning Workshop (camera ready version). The full-length paper, which includes a number of additional algorithms and results (including proofs of statements and experiments), is available in arXiv:1706.07636 

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LEAF: A Benchmark for Federated Settings

Jan 09, 2019
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar


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Federated Learning: Strategies for Improving Communication Efficiency

Oct 30, 2017
Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, Dave Bacon


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Stochastic, Distributed and Federated Optimization for Machine Learning

Jul 04, 2017
Jakub Konečný

* PhD thesis 

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Randomized Distributed Mean Estimation: Accuracy vs Communication

Nov 22, 2016
Jakub Konečný, Peter Richtárik

* 19 pages, 1 figure 

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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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AIDE: Fast and Communication Efficient Distributed Optimization

Aug 24, 2016
Sashank J. Reddi, Jakub Konečný, Peter Richtárik, Barnabás Póczós, Alex Smola


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Distributed Optimization with Arbitrary Local Solvers

Aug 03, 2016
Chenxin Ma, Jakub Konečný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takáč


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Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting

Nov 16, 2015
Jakub Konečný, Jie Liu, Peter Richtárik, Martin Takáč


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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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Stop Wasting My Gradients: Practical SVRG

Nov 05, 2015
Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konečný, Scott Sallinen


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Semi-Stochastic Gradient Descent Methods

Jun 16, 2015
Jakub Konečný, Peter Richtárik

* 19 pages, 3 figures, 2 algorithms, 3 tables 

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mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting

Oct 17, 2014
Jakub Konečný, Jie Liu, Peter Richtárik, Martin Takáč


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One-Shot-Learning Gesture Recognition using HOG-HOF Features

Feb 15, 2014
Jakub Konečný, Michal Hagara

* 20 pages, 10 figures, 2 tables To appear in Journal of Machine Learning Research subject to minor revision 

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