| Federated learning: Challenges, methods, and future directions T Li, AK Sahu, A Talwalkar, V Smith IEEE Signal Processing Magazine 37 (3), 50-60, 2020 | 671 | 2020 |
| Federated optimization in heterogeneous networks T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith arXiv preprint arXiv:1812.06127, 2018 | 477 | 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 | 224 | 2018 |
| Fair resource allocation in federated learning T Li, M Sanjabi, A Beirami, V Smith arXiv preprint arXiv:1905.10497, 2019 | 151 | 2019 |
| 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 | 42 | 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 | 29 | 2019 |
| Enhancing the privacy of federated learning with sketching Z Liu, T Li, V Smith, V Sekar arXiv preprint arXiv:1911.01812, 2019 | 10 | 2019 |
| 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 | 9 | 2020 |
| 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 |
| Tilted empirical risk minimization T Li, A Beirami, M Sanjabi, V Smith arXiv preprint arXiv:2007.01162, 2020 | 7 | 2020 |
| Ditto: Fair and Robust Federated Learning Through Personalization T Li, S Hu, A Beirami, V Smith arXiv preprint arXiv:2012.04221, 2020 | 5* | 2020 |
| Ease. ML: A Lifecycle Management System for Machine Learning L Aguilar Melgar, D Dao, S Gan, NM Gürel, N Hollenstein, J Jiang, ... 11th Annual Conference on Innovative Data Systems Research (CIDR 2021)(virtual), 2021 | 2 | 2021 |
| Heterogeneity for the Win: One-Shot Federated Clustering DK Dennis, T Li, V Smith arXiv preprint arXiv:2103.00697, 2021 | 1 | 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 | 1 | 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 | | 2021 |
| Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing M Khodak, R Tu, T Li, L Li, MF Balcan, V Smith, A Talwalkar arXiv preprint arXiv:2106.04502, 2021 | | 2021 |
| Ease. ML: A Lifecycle Management System for Machine Learning L Aguilar, D Dao, S Gan, NM Gurel, N Hollenstein, J Jiang, B Karlas, ... | | 2020 |
| CUTE: Querying Knowledge Graphs by Tabular Examples Z Wang, T Li, Y Shao, B Cui Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint …, 2018 | | 2018 |
Virginia SmithCarnegie Mellon UniversityVerified email at cmu.edu
