| Advances and open problems in federated learning P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Foundations and Trends® in Machine Learning 14, 1-210, 2019 | 7106 | 2019 |
| Federated Learning: Strategies for Improving Communication Efficiency J Konečný arXiv preprint arXiv:1610.05492, 2016 | 5911 | 2016 |
| Towards federated learning at scale: System design K Bonawitz, H Eichner, W Grieskamp, D Huba, A Ingerman, V Ivanov, ... Proceedings of the 2nd SysML Conference, 2019 | 3524 | 2019 |
| Federated optimization: Distributed machine learning for on-device intelligence J Konečný, HB McMahan, D Ramage, P Richtárik arXiv preprint arXiv:1610.02527, 2016 | 2486 | 2016 |
| Adaptive federated optimization S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečný, S Kumar, ... International Conference on Learning Representations, 2020 | 1636 | 2020 |
| Leaf: A benchmark for federated settings S Caldas, SMK Duddu, P Wu, T Li, J Konečný, HB McMahan, V Smith, ... arXiv preprint arXiv:1812.01097, 2018 | 1586 | 2018 |
| Federated optimization: Distributed optimization beyond the datacenter J Konečný, B McMahan, D Ramage arXiv preprint arXiv:1511.03575, 2015 | 868 | 2015 |
| Improving federated learning personalization via model agnostic meta learning Y Jiang, J Konečný, K Rush, S Kannan arXiv preprint arXiv:1909.12488, 2019 | 697 | 2019 |
| Expanding the Reach of Federated Learning by Reducing Client Resource Requirements S Caldas, J Konečný, HB McMahan, A Talwalkar arXiv preprint arXiv:1812.07210, 2018 | 543 | 2018 |
| A field guide to federated optimization J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ... arXiv preprint arXiv:2107.06917, 2021 | 398 | 2021 |
| Mini-batch semi-stochastic gradient descent in the proximal setting J Konečný, J Liu, P Richtárik, M Takáč IEEE Journal of Selected Topics in Signal Processing 10 (2), 242-255, 2015 | 345 | 2015 |
| Semi-stochastic gradient descent methods J Konečný, P Richtárik Frontiers in Applied Mathematics and Statistics 3, 2017 | 282 | 2017 |
| Distributed optimization with arbitrary local solvers C Ma, J Konečný, M Jaggi, V Smith, MI Jordan, P Richtárik, M Takáč Optimization Methods and Software 32 (4), 813-848, 2015 | 237 | 2015 |
| Aide: Fast and communication efficient distributed optimization SJ Reddi, J Konečný, P Richtárik, B Póczós, A Smola arXiv preprint arXiv:1608.06879, 2016 | 184 | 2016 |
| Stop Wasting My Gradients: Practical SVRG R Harikandeh, MO Ahmed, A Virani, M Schmidt, J Konečný, S Sallinen Advances in Neural Information Processing Systems, 2242-2250, 2015 | 168* | 2015 |
| Randomized distributed mean estimation: Accuracy vs. communication J Konečný, P Richtárik Frontiers in Applied Mathematics and Statistics 4, 62, 2018 | 111 | 2018 |
| One-Shot-Learning Gesture Recognition using HOG-HOF Features J Konečný, M Hagara Journal of Machine Learning Research 15, 2513-2532, 2013 | 108 | 2013 |
| Semi-stochastic coordinate descent J Konečný, Z Qu, P Richtárik Optimization Methods and Software 32 (5), 993-1005, 2017 | 104 | 2017 |
| Federated learning with autotuned communication-efficient secure aggregation K Bonawitz, F Salehi, J Konečný, B McMahan, M Gruteser 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 1222-1226, 2019 | 91 | 2019 |
| Convergence and accuracy trade-offs in federated learning and meta-learning Z Charles, J Konečný International Conference on Artificial Intelligence and Statistics, 2575-2583, 2021 | 84 | 2021 |
H. Brendan McMahanResearch Scientist, Google SeattleVerified email at google.com