mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. - Jakub Konecný; Jie Liu 036; Peter Richtárik; Martin Takác 001 (CoRR, 2014)

Simple Complexity Analysis of Direct Search. - Jakub Konecný; Peter Richtárik (CoRR, 2014)

One-shot-learning gesture recognition using HOG-HOF features. - Jakub Konecný; Michal Hagara (J. Mach. Learn. Res., 2014)

StopWasting My Gradients: Practical SVRG. - Reza Babanezhad 001; Mohamed Osama Ahmed; Alim Virani; Mark Schmidt 001; Jakub Konecný; Scott Sallinen (NIPS, 2015)

Stop Wasting My Gradients: Practical SVRG. - Reza Babanezhad 001; Mohamed Osama Ahmed; Alim Virani; Mark Schmidt 001; Jakub Konecný; Scott Sallinen (CoRR, 2015)

Federated Optimization: Distributed Optimization Beyond the Datacenter. - Jakub Konecný; Brendan McMahan; Daniel Ramage (CoRR, 2015)

Federated Optimization: Distributed Machine Learning for On-Device Intelligence. - Jakub Konecný; H. Brendan McMahan; Daniel Ramage; Peter Richtárik (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)

AIDE: Fast and Communication Efficient Distributed Optimization. - Sashank J. Reddi; Jakub Konecný; Peter Richtárik; Barnabás Póczos; Alexander J. Smola (CoRR, 2016)

Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting. - Jakub Konecný; Jie Liu 036; Peter Richtárik; Martin Takác 001 (IEEE J. Sel. Top. Signal Process., 2016)

One-Shot-Learning Gesture Recognition Using HOG-HOF Features. - Jakub Konecný; Michal Hagara (Gesture Recognition, 2017)

On practical deployment of smart card based authenticated key agreement schemes. - Lukas Malina; Jakub Konecný; Petr Dzurenda; Jan Hajny (ICUMT, 2017)

Stochastic, Distributed and Federated Optimization for Machine Learning. - Jakub Konecný (CoRR, 2017)

Semi-Stochastic Gradient Descent Methods. - Jakub Konecný; Peter Richtárik (Frontiers Appl. Math. Stat., 2017)

Semi-stochastic coordinate descent. - Jakub Konecný; Zheng Qu 001; Peter Richtárik (Optim. Methods Softw., 2017)

Distributed optimization with arbitrary local solvers. - Chenxin Ma; Jakub Konecný; Martin Jaggi; Virginia Smith; Michael I. Jordan; Peter Richtárik; Martin Takác 001 (Optim. Methods Softw., 2017)

Stochastic, distributed and federated optimization for machine learning - Jakub Konecný (2017)

LEAF: A Benchmark for Federated Settings. - Sebastian Caldas; Peter Wu; Tian Li 005; Jakub Konecný; H. Brendan McMahan; Virginia Smith; Ameet Talwalkar (CoRR, 2018)

Expanding the Reach of Federated Learning by Reducing Client Resource Requirements. - Sebastian Caldas; Jakub Konecný; H. Brendan McMahan; Ameet Talwalkar (CoRR, 2018)

Randomized Distributed Mean Estimation: Accuracy vs. Communication. - Jakub Konecný; Peter Richtárik (Frontiers Appl. Math. Stat., 2018)

Federated Learning with Autotuned Communication-Efficient Secure Aggregation. - Kallista A. Bonawitz; Fariborz Salehi; Jakub Konecný; Brendan McMahan; Marco Gruteser (ACSSC, 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; et al. (SysML, 2019)

A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion. - Filip Hanzely; Jakub Konecný; Nicolas Loizou; Peter Richtárik; Dmitry Grishchenko (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; et al. (CoRR, 2019)

Improving Federated Learning Personalization via Model Agnostic Meta Learning. - Yihan Jiang; Jakub Konecný; Keith Rush; Sreeram Kannan (CoRR, 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; et al. (CoRR, 2019)

On the Outsized Importance of Learning Rates in Local Update Methods. - Zachary Charles; Jakub Konecný (CoRR, 2020)

Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning. - Zachary Charles; Jakub Konecný (AISTATS, 2021)

Adaptive Federated Optimization. - Sashank J. Reddi; Zachary Charles; Manzil Zaheer; Zachary Garrett; Keith Rush; Jakub Konecný; Sanjiv Kumar; Hugh Brendan McMahan (ICLR, 2021)

A Field Guide to Federated Optimization. - Jianyu Wang; Zachary Charles; Zheng Xu 002; Gauri Joshi; H. Brendan McMahan; Blaise Agüera y Arcas; Maruan Al-Shedivat; Galen Andrew; Salman Avestimehr; Katharine Daly; Deepesh Data; Suhas N. Diggavi; et al. (CoRR, 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; et al. (Found. Trends Mach. Learn., 2021)

Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions. - Chen Zhu 001; Zheng Xu 002; Mingqing Chen; Jakub Konecný; Andrew Hard; Tom Goldstein (ICLR, 2022)

Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory. - Nicole Mitchell; Johannes Ballé; Zachary Charles; Jakub Konecný (CoRR, 2022)

A Rate-Distortion View on Model Updates. - Nicole Mitchell; Johannes Ballé; Zachary Charles; Jakub Konecný (Tiny Papers @ ICLR, 2023)