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 | 5631 | 2021 |
The composition theorem for differential privacy P Kairouz, S Oh, P Viswanath IEEE Transactions on Information Theory 63 (6), 4037-4049, 2017 | 748 | 2017 |
Extremal mechanisms for local differential privacy P Kairouz, S Oh, P Viswanath Journal of Machine Learning Research 17 (17), 1-51, 2016 | 634 | 2016 |
Can you really backdoor federated learning? Z Sun, P Kairouz, AT Suresh, HB McMahan arXiv preprint arXiv:1911.07963, 2019 | 574 | 2019 |
Discrete distribution estimation under local privacy P Kairouz, K Bonawitz, D Ramage International Conference on Machine Learning, 2436-2444, 2016 | 364 | 2016 |
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 | 332 | 2021 |
Back to the drawing board: A critical evaluation of poisoning attacks on production federated learning V Shejwalkar, A Houmansadr, P Kairouz, D Ramage 2022 IEEE Symposium on Security and Privacy (SP), 1354-1371, 2022 | 248 | 2022 |
Dp-cgan: Differentially private synthetic data and label generation R Torkzadehmahani, P Kairouz, B Paten Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 245 | 2019 |
The distributed discrete gaussian mechanism for federated learning with secure aggregation P Kairouz, Z Liu, T Steinke International Conference on Machine Learning, 5201-5212, 2021 | 213 | 2021 |
A general approach to adding differential privacy to iterative training procedures HB McMahan, G Andrew, U Erlingsson, S Chien, I Mironov, N Papernot, ... arXiv preprint arXiv:1812.06210, 2018 | 210 | 2018 |
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 | 198 | 2019 |
The staircase mechanism in differential privacy Q Geng, P Kairouz, S Oh, P Viswanath IEEE Journal of Selected Topics in Signal Processing 9 (7), 1176-1184, 2015 | 185 | 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 | 183 | 2021 |
Context-aware generative adversarial privacy C Huang, P Kairouz, X Chen, L Sankar, R Rajagopal Entropy 19 (12), 656, 2017 | 172 | 2017 |
Mariana Raykova, Dawn Song, Weikang Song, Sebastian U P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth …, 2021 | 170 | 2021 |
Practical and private (deep) learning without sampling or shuffling P Kairouz, B McMahan, S Song, O Thakkar, A Thakurta, Z Xu International Conference on Machine Learning, 5213-5225, 2021 | 155 | 2021 |
Breaking the communication-privacy-accuracy trilemma WN Chen, P Kairouz, A Ozgur Advances in Neural Information Processing Systems 33, 3312-3324, 2020 | 121 | 2020 |
The skellam mechanism for differentially private federated learning N Agarwal, P Kairouz, Z Liu Advances in Neural Information Processing Systems 34, 5052-5064, 2021 | 113 | 2021 |
Federated heavy hitters discovery with differential privacy W Zhu, P Kairouz, B McMahan, H Sun, W Li International Conference on Artificial Intelligence and Statistics, 3837-3847, 2020 | 112 | 2020 |
Privacy-first health research with federated learning A Sadilek, L Liu, D Nguyen, M Kamruzzaman, S Serghiou, B Rader, ... NPJ digital medicine 4 (1), 132, 2021 | 91 | 2021 |