| Communication-efficient learning of deep networks from decentralized data HB McMahan, E Moore, D Ramage, S Hampson, B Ag�era y Arcas Proceedings of the 20 th International Conference on Artificial Intelligence�…, 2017 | 20294 | 2017 |
| Deep learning with differential privacy M Abadi, A Chu, I Goodfellow, HB McMahan, I Mironov, K Talwar, L Zhang Proceedings of the 2016 ACM SIGSAC conference on computer and communications�…, 2016 | 6969 | 2016 |
| 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 (1–2), 1-210, 2021 | 6474 | 2021 |
| Federated Learning: Strategies for Improving Communication Efficiency J Konečn� arXiv preprint arXiv:1610.05492, 2016 | 5710 | 2016 |
| Practical secure aggregation for privacy-preserving machine learning K Bonawitz, V Ivanov, B Kreuter, A Marcedone, HB McMahan, S Patel, ... proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications�…, 2017 | 3394 | 2017 |
| Towards federated learning at scale: Syste m design K Bonawitz arXiv preprint arXiv:1902.01046, 2019 | 3285 | 2019 |
| Federated optimization: Distributed machine learning for on-device intelligence J Konečn�, HB McMahan, D Ramage, P Richt�rik arXiv preprint arXiv:1610.02527, 2016 | 2358 | 2016 |
| Learning differentially private recurrent language models HB McMahan, D Ramage, K Talwar, L Zhang arXiv preprint arXiv:1710.06963, 2017 | 1595 | 2017 |
| Adaptive federated optimization S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečn�, S Kumar, ... arXiv preprint arXiv:2003.00295, 2020 | 1505 | 2020 |
| Leaf: A benchmark for federated settings S Caldas, SMK Duddu, P Wu, T Li, J Konečn�, HB McMahan, V Smith, ... arXiv preprint arXiv:1812.01097, 2018 | 1496 | 2018 |
| Ad click prediction: a view from the trenches HB McMahan, G Holt, D Sculley, M Young, D Ebner, J Grady, L Nie, ... Proceedings of the 19th ACM SIGKDD international conference on Knowledge�…, 2013 | 1148 | 2013 |
| Online convex optimization in the bandit setting: gradient descent without a gradient AD Flaxman, AT Kalai, HB McMahan arXiv preprint cs/0408007, 2004 | 1022 | 2004 |
| Federated optimization: Distributed optimization beyond the datacenter J Konečn�, B McMahan, D Ramage arXiv preprint arXiv:1511.03575, 2015 | 813 | 2015 |
| Federated learning: Collaborative machine learning without centralized training data B McMahan, D Ramage Google Research Blog 3, 2017 | 801 | 2017 |
| Can you really backdoor federated learning? Z Sun, P Kairouz, AT Suresh, HB McMahan arXiv preprint arXiv:1911.07963, 2019 | 645 | 2019 |
| Practical secure aggregation for federated learning on user-held data K Bonawitz, V Ivanov, B Kreuter, A Marcedone, HB McMahan, S Patel, ... arXiv preprint arXiv:1611.04482, 2016 | 573 | 2016 |
| cpSGD: Communication-efficient and differentially-private distributed SGD N Agarwal, AT Suresh, FXX Yu, S Kumar, B McMahan Advances in Neural Information Processing Systems 31, 2018 | 542 | 2018 |
| Expanding the reach of federated learning by reducing client resource requirements S Caldas, J Konečny, HB McMahan, A Talwalkar arXiv preprint arXiv:1812.07210, 2018 | 495 | 2018 |
| Adaptive bound optimization for online convex optimization HB McMahan, M Streeter Proceedings of the 23rd Annual Conference on Learning Theory (COLT), 2010 | 425 | 2010 |
| Planning in the presence of cost functions controlled by an adversary HB McMahan, GJ Gordon, A Blum Proceedings of the 20th International Conference on Machine Learning (ICML�…, 2003 | 405 | 2003 |
Daniel RamageGoogle ResearchVerified email at google.com
