| Federated learning: Strategies for improving communication efficiency J Konečný, HB McMahan, FX Yu, P Richtárik, AT Suresh, D Bacon arXiv preprint arXiv:1610.05492, 2016 | 4106 | 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 | 3981 | 2021 |
| Scaffold: Stochastic controlled averaging for federated learning SP Karimireddy, S Kale, M Mohri, S Reddi, S Stich, AT Suresh International conference on machine learning, 5132-5143, 2020 | 1621 | 2020 |
| Agnostic federated learning M Mohri, G Sivek, AT Suresh International Conference on Machine Learning, 4615-4625, 2019 | 730 | 2019 |
| Three approaches for personalization with applications to federated learning Y Mansour, M Mohri, J Ro, AT Suresh arXiv preprint arXiv:2002.10619, 2020 | 421 | 2020 |
| 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 | 418 | 2018 |
| Can you really backdoor federated learning? Z Sun, P Kairouz, AT Suresh, HB McMahan arXiv preprint arXiv:1911.07963, 2019 | 415 | 2019 |
| Distributed mean estimation with limited communication AT Suresh, XY Felix, S Kumar, HB McMahan International conference on machine learning, 3329-3337, 2017 | 301 | 2017 |
| Orthogonal random features XY Felix, AT Suresh, KM Choromanski, DN Holtmann-Rice, S Kumar Advances in Neural Information Processing Systems, 1975-1983, 2016 | 214* | 2016 |
| Breaking the centralized barrier for cross-device federated learning SP Karimireddy, M Jaggi, S Kale, M Mohri, S Reddi, SU Stich, AT Suresh Advances in Neural Information Processing Systems 34, 28663-28676, 2021 | 175* | 2021 |
| Fedboost: A communication-efficient algorithm for federated learning J Hamer, M Mohri, AT Suresh International Conference on Machine Learning, 3973-3983, 2020 | 157 | 2020 |
| 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 | 141 | 2021 |
| Optimal prediction of the number of unseen species A Orlitsky, AT Suresh, Y Wu Proceedings of the National Academy of Sciences 113 (47), 13283-13288, 2016 | 139 | 2016 |
| On learning distributions from their samples S Kamath, A Orlitsky, D Pichapati, AT Suresh Conference on Learning Theory, 1066-1100, 2015 | 121 | 2015 |
| Shuffled model of differential privacy in federated learning A Girgis, D Data, S Diggavi, P Kairouz, AT Suresh International Conference on Artificial Intelligence and Statistics, 2521-2529, 2021 | 110 | 2021 |
| Adaclip: Adaptive clipping for private sgd V Pichapati, AT Suresh, FX Yu, SJ Reddi, S Kumar arXiv preprint arXiv:1908.07643, 2019 | 98 | 2019 |
| Remember what you want to forget: Algorithms for machine unlearning A Sekhari, J Acharya, G Kamath, AT Suresh Advances in Neural Information Processing Systems 34, 18075-18086, 2021 | 94 | 2021 |
| Near-optimal-sample estimators for spherical gaussian mixtures AT Suresh, A Orlitsky, J Acharya, A Jafarpour Advances in Neural Information Processing Systems 27, 2014 | 92 | 2014 |
| Competitive distribution estimation: Why is good-turing good A Orlitsky, AT Suresh Advances in Neural Information Processing Systems 28, 2015 | 90 | 2015 |
| Model-powered conditional independence test R Sen, AT Suresh, K Shanmugam, AG Dimakis, S Shakkottai Advances in neural information processing systems 30, 2017 | 85 | 2017 |
Felix Xinnan YuResearch Scientist, Google New YorkVerified email at google.com
