Jakub Konecný

According to our database, Jakub Konecný authored at least 34 papers between 2014 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

A Rate-Distortion View on Model Updates.

Nicole Mitchell, Johannes Ballé, Zachary Charles, Jakub Konecný

Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

2022

Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory.

Nicole Mitchell, Johannes Ballé, Zachary Charles, Jakub Konecný

CoRR, 2022

Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions.

Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konecný, Andrew Hard, Tom Goldstein

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Advances and Open Problems in Federated Learning.

Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, 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

Found. Trends Mach. Learn., 2021

A Field Guide to Federated Optimization.

Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu

CoRR, 2021

Adaptive Federated Optimization.

Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, Hugh Brendan McMahan

Proceedings of the 9th International Conference on Learning Representations, 2021

Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning.

Zachary Charles, Jakub Konecný

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

On the Outsized Importance of Learning Rates in Local Update Methods.

Zachary Charles, Jakub Konecný

CoRR, 2020

2019

Advances and Open Problems in Federated Learning.

Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista 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, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, 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

CoRR, 2019

Improving Federated Learning Personalization via Model Agnostic Meta Learning.

Yihan Jiang, Jakub Konecný, Keith Rush, Sreeram Kannan

CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.

Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. 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 M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar

CoRR, 2019

A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion.

Filip Hanzely, Jakub Konecný, Nicolas Loizou, Peter Richtárik, Dmitry Grishchenko

CoRR, 2019

Towards Federated Learning at Scale: System Design.

Kallista A. Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé Kiddon, Jakub Konecný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander

Proceedings of the Second Conference on Machine Learning and Systems, SysML 2019, 2019

Federated Learning with Autotuned Communication-Efficient Secure Aggregation.

Kallista A. Bonawitz, Fariborz Salehi, Jakub Konecný, Brendan McMahan, Marco Gruteser

Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018

Randomized Distributed Mean Estimation: Accuracy vs. Communication.

Jakub Konecný, Peter Richtárik

Frontiers Appl. Math. Stat., 2018

Expanding the Reach of Federated Learning by Reducing Client Resource Requirements.

Sebastian Caldas, Jakub Konecný, H. Brendan McMahan, Ameet Talwalkar

CoRR, 2018

LEAF: A Benchmark for Federated Settings.

Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar

CoRR, 2018

2017

Stochastic, distributed and federated optimization for machine learning

Jakub Konecný

PhD thesis, 2017

Distributed optimization with arbitrary local solvers.

Chenxin Ma, Jakub Konecný, Martin Jaggi, Virginia Smith, Michael I. Jordan, Peter Richtárik, Martin Takác

Optim. Methods Softw., 2017

Semi-stochastic coordinate descent.

Jakub Konecný, Zheng Qu, Peter Richtárik

Optim. Methods Softw., 2017

Semi-Stochastic Gradient Descent Methods.

Jakub Konecný, Peter Richtárik

Frontiers Appl. Math. Stat., 2017

Stochastic, Distributed and Federated Optimization for Machine Learning.

Jakub Konecný

CoRR, 2017

On practical deployment of smart card based authenticated key agreement schemes.

Lukas Malina, Jakub Konecný, Petr Dzurenda, Jan Hajny

Proceedings of the 9th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, 2017

One-Shot-Learning Gesture Recognition Using HOG-HOF Features.

Jakub Konecný, Michal Hagara

Proceedings of the Gesture Recognition, 2017

2016

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

Jakub Konecný, Jie Liu, Peter Richtárik, Martin Takác

IEEE J. Sel. Top. Signal Process., 2016

AIDE: Fast and Communication Efficient Distributed Optimization.

Sashank J. Reddi, Jakub Konecný, Peter Richtárik, Barnabás Póczos, Alexander J. Smola

CoRR, 2016

Federated Learning: Strategies for Improving Communication Efficiency.

Jakub Konecný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, Dave Bacon

CoRR, 2016

Federated Optimization: Distributed Machine Learning for On-Device Intelligence.

Jakub Konecný, H. Brendan McMahan, Daniel Ramage, Peter Richtárik

CoRR, 2016

2015

Federated Optimization: Distributed Optimization Beyond the Datacenter.

Jakub Konecný, Brendan McMahan, Daniel Ramage

CoRR, 2015

Stop Wasting My Gradients: Practical SVRG.

Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen

CoRR, 2015

StopWasting My Gradients: Practical SVRG.

Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

One-shot-learning gesture recognition using HOG-HOF features.

Jakub Konecný, Michal Hagara

J. Mach. Learn. Res., 2014

Simple Complexity Analysis of Direct Search.

Jakub Konecný, Peter Richtárik

CoRR, 2014

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

Jakub Konecný, Jie Liu, Peter Richtárik, Martin Takác

CoRR, 2014