| Federated optimization in heterogeneous networks T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith Conference on Machine Learning and Systems, 2020 | 6381 | 2020 |
| Federated learning: Challenges, methods, and future directions T Li, AK Sahu, A Talwalkar, V Smith IEEE signal processing magazine 37 (3), 50-60, 2020 | 5906 | 2020 |
| Federated multi-task learning V Smith, CK Chiang, M Sanjabi, A Talwalkar Advances in Neural Information Processing Systems, 2017 | 2333 | 2017 |
| 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 | 1608 | 2018 |
| Ditto: Fair and robust federated learning through personalization T Li, S Hu, A Beirami, V Smith International conference on machine learning, 6357-6368, 2021 | 1075 | 2021 |
| Fair resource allocation in federated learning T Li, M Sanjabi, A Beirami, V Smith International Conference on Learning Representations, 2020 | 1012 | 2020 |
| Communication-efficient distributed dual coordinate ascent M Jaggi*, V Smith*, M Takác, J Terhorst, S Krishnan, T Hofmann, ... Advances in Neural Information Processing Systems, 2014 | 429 | 2014 |
| 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 | 416 | 2021 |
| CoCoA: A general framework for communication-efficient distributed optimization V Smith, S Forte, C Ma, M Takáč, MI Jordan, M Jaggi Journal of Machine Learning Research 18 (230), 1-49, 2018 | 330 | 2018 |
| One-shot federated learning N Guha, A Talwalkar, V Smith arXiv preprint arXiv:1902.11175, 2019 | 267 | 2019 |
| MLI: An API for distributed machine learning ER Sparks, A Talwalkar, V Smith, J Kottalam, X Pan, J Gonzalez, ... IEEE International Conference on Data Mining, 2013 | 246 | 2013 |
| 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, 2017 | 242 | 2017 |
| A kernel theory of modern data augmentation T Dao, A Gu, A Ratner, V Smith, C De Sa, C Ré International conference on machine learning, 1528-1537, 2019 | 239 | 2019 |
| Adding vs. averaging in distributed primal-dual optimization C Ma*, V Smith*, M Jaggi, MI Jordan, P Richtárik, ... International Conference on Machine Learning, 2015 | 212 | 2015 |
| Feddane: A federated newton-type method T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smithy 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 1227-1231, 2019 | 197 | 2019 |
| Heterogeneity for the win: One-shot federated clustering DK Dennis, T Li, V Smith International conference on machine learning, 2611-2620, 2021 | 186 | 2021 |
| Label leakage and protection in two-party split learning O Li, J Sun, X Yang, W Gao, H Zhang, J Xie, V Smith, C Wang arXiv preprint arXiv:2102.08504, 2021 | 162 | 2021 |
| Tilted empirical risk minimization T Li, A Beirami, M Sanjabi, V Smith arXiv preprint arXiv:2007.01162, 2020 | 158 | 2020 |
| Diverse client selection for federated learning via submodular maximization R Balakrishnan, T Li, T Zhou, N Himayat, V Smith, J Bilmes International Conference on Learning Representations, 2022 | 132 | 2022 |
| On large-cohort training for federated learning Z Charles, Z Garrett, Z Huo, S Shmulyian, V Smith Advances in neural information processing systems 34, 20461-20475, 2021 | 124 | 2021 |
Tian LiUniversity of Chicago在 uchicago.edu 的电子邮件经过验证