| Federated learning for mobile keyboard prediction A Hard, K Rao, R Mathews, S Ramaswamy, F Beaufays, S Augenstein, ... arXiv preprint arXiv:1811.03604, 2018 | 1574 | 2018 |
| Federated learning for emoji prediction in a mobile keyboard S Ramaswamy, R Mathews, K Rao, F Beaufays arXiv preprint arXiv:1906.04329, 2019 | 321 | 2019 |
| Federated evaluation of on-device personalization K Wang, R Mathews, C Kiddon, H Eichner, F Beaufays, D Ramage arXiv preprint arXiv:1910.10252, 2019 | 313 | 2019 |
| Interactive text message advertisements SS Belwadi, S Sundaram, KL Yong, N Singh, R Mathews US Patent 8,521,581, 2013 | 228 | 2013 |
| Generative models for effective ML on private, decentralized datasets S Augenstein, HB McMahan, D Ramage, S Ramaswamy, P Kairouz, ... arXiv preprint arXiv:1911.06679, 2019 | 195 | 2019 |
| Federated learning of out-of-vocabulary words M Chen, R Mathews, T Ouyang, F Beaufays arXiv preprint arXiv:1903.10635, 2019 | 180 | 2019 |
| Federated learning of n-gram language models M Chen, AT Suresh, R Mathews, A Wong, C Allauzen, F Beaufays, ... arXiv preprint arXiv:1910.03432, 2019 | 82 | 2019 |
| Training production language models without memorizing user data S Ramaswamy, O Thakkar, R Mathews, G Andrew, HB McMahan, ... arXiv preprint arXiv:2009.10031, 2020 | 71 | 2020 |
| Federated learning for mobile keyboard prediction (2018) A Hard, K Rao, R Mathews, S Ramaswamy, F Beaufays, S Augenstein, ... arXiv preprint arXiv:1811.03604, 1811 | 59 | 1811 |
| Training keyword spotting models on non-iid data with federated learning A Hard, K Partridge, C Nguyen, N Subrahmanya, A Shah, P Zhu, ... arXiv preprint arXiv:2005.10406, 2020 | 53 | 2020 |
| Public data-assisted mirror descent for private model training E Amid, A Ganesh, R Mathews, S Ramaswamy, S Song, T Steinke, ... International Conference on Machine Learning, 517-535, 2022 | 50 | 2022 |
| Federated learning for mobile keyboard prediction. arXiv 2018 A Hard, K Rao, R Mathews, S Ramaswamy, F Beaufays, S Augenstein, ... arXiv preprint arXiv:1811.03604, 2018 | 40 | 2018 |
| Revealing and protecting labels in distributed training T Dang, O Thakkar, S Ramaswamy, R Mathews, P Chin, F Beaufays Advances in Neural Information Processing Systems 34, 1727-1738, 2021 | 33 | 2021 |
| Understanding unintended memorization in federated learning O Thakkar, S Ramaswamy, R Mathews, F Beaufays arXiv preprint arXiv:2006.07490, 2020 | 29 | 2020 |
| Understanding unintended memorization in language models under federated learning OD Thakkar, S Ramaswamy, R Mathews, F Beaufays Proceedings of the Third Workshop on Privacy in Natural Language Processing …, 2021 | 27 | 2021 |
| Scaling language model size in cross-device federated learning JH Ro, T Breiner, L McConnaughey, M Chen, AT Suresh, S Kumar, ... arXiv preprint arXiv:2204.09715, 2022 | 22 | 2022 |
| Communication-efficient agnostic federated averaging J Ro, M Chen, R Mathews, M Mohri, AT Suresh arXiv preprint arXiv:2104.02748, 2021 | 14 | 2021 |
| Userlibri: A dataset for asr personalization using only text T Breiner, S Ramaswamy, E Variani, S Garg, R Mathews, KC Sim, ... arXiv preprint arXiv:2207.00706, 2022 | 13 | 2022 |
| Production federated keyword spotting via distillation, filtering, and joint federated-centralized training A Hard, K Partridge, N Chen, S Augenstein, A Shah, HJ Park, A Park, ... arXiv preprint arXiv:2204.06322, 2022 | 13 | 2022 |
| Federated learning for emoji prediction in a mobile keyboard. arXiv 2019 S Ramaswamy, R Mathews, K Rao, F Beaufays arXiv preprint arXiv:1906.04329, 0 | 13 | |
