| Federated learning: Challenges, methods, and future directions T Li, AK Sahu, A Talwalkar, V Smith IEEE Signal Processing Magazine 37 (3), 50-60, 2020 | 1676 | 2020 |
| Federated optimization in heterogeneous networks T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith arXiv preprint arXiv:1812.06127, 2018 | 1308 | 2018 |
| Leaf: A benchmark for federated settings S Caldas, SMK Duddu, P Wu, T Li, J Konečnı, HB McMahan, V Smith, ... arXiv preprint arXiv:1812.01097, 2018 | 551 | 2018 |
| Fair resource allocation in federated learning T Li, M Sanjabi, A Beirami, V Smith arXiv preprint arXiv:1905.10497, 2019 | 363 | 2019 |
| Ditto: Fair and Robust Federated Learning Through Personalization T Li, S Hu, A Beirami, V Smith arXiv preprint arXiv:2012.04221, 2020 | 138 | 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 | 96 | 2021 |
| Ease. ml: Towards multi-tenant resource sharing for machine learning workloads T Li, J Zhong, J Liu, W Wu, C Zhang Proceedings of the VLDB Endowment 11 (5), 607-620, 2018 | 67 | 2018 |
| Feddane: A federated newton-type method T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 1227-1231, 2019 | 59 | 2019 |
| Tilted empirical risk minimization T Li, A Beirami, M Sanjabi, V Smith arXiv preprint arXiv:2007.01162, 2020 | 42 | 2020 |
| Learning context-aware policies from multiple smart homes via federated multi-task learning T Yu, T Li, Y Sun, S Nanda, V Smith, V Sekar, S Seshan 2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design …, 2020 | 34 | 2020 |
| Heterogeneity for the win: One-shot federated clustering DK Dennis, T Li, V Smith International Conference on Machine Learning, 2611-2620, 2021 | 24 | 2021 |
| Enhancing the privacy of federated learning with sketching Z Liu, T Li, V Smith, V Sekar arXiv preprint arXiv:1911.01812, 2019 | 17 | 2019 |
| Federated hyperparameter tuning: Challenges, baselines, and connections to weight-sharing M Khodak, R Tu, T Li, L Li, MFF Balcan, V Smith, A Talwalkar Advances in Neural Information Processing Systems 34, 19184-19197, 2021 | 14 | 2021 |
| Ease. ml: A lifecycle management system for machine learning L Aguilar Melgar, D Dao, S Gan, NM Gürel, N Hollenstein, J Jiang, ... Proceedings of the Annual Conference on Innovative Data Systems Research …, 2021 | 12* | 2021 |
| An overreaction to the broken machine learning abstraction: The ease. ml vision C Zhang, W Wu, T Li Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics, 1-6, 2017 | 8 | 2017 |
| On tilted losses in machine learning: Theory and applications T Li, A Beirami, M Sanjabi, V Smith arXiv preprint arXiv:2109.06141, 2021 | 6 | 2021 |
| 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, 2021 | 5 | 2021 |
| Diverse client selection for federated learning: Submodularity and convergence analysis R Balakrishnan, T Li, T Zhou, N Himayat, V Smith, J Bilmes ICML 2021 International Workshop on Federated Learning for User Privacy and …, 2021 | 5 | 2021 |
| Weight sharing for hyperparameter optimization in federated learning M Khodak, T Li, L Li, M Balcan, V Smith, A Talwalkar Int. Workshop on Federated Learning for User Privacy and Data …, 2020 | 5 | 2020 |
| Private adaptive optimization with side information T Li, M Zaheer, S Reddi, V Smith International Conference on Machine Learning, 13086-13105, 2022 | 1 | 2022 |
Virginia SmithCarnegie Mellon UniversityVerified email at cmu.edu
