Tian Li
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Federated learning: Challenges, methods, and future directions
T Li, AK Sahu, A Talwalkar, V Smith
IEEE Signal Processing Magazine 37 (3), 50-60, 2020
Federated optimization in heterogeneous networks
T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith
arXiv preprint arXiv:1812.06127, 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
Fair resource allocation in federated learning
T Li, M Sanjabi, A Beirami, V Smith
arXiv preprint arXiv:1905.10497, 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
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
Enhancing the privacy of federated learning with sketching
Z Liu, T Li, V Smith, V Sekar
arXiv preprint arXiv:1911.01812, 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
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
Tilted empirical risk minimization
T Li, A Beirami, M Sanjabi, V Smith
arXiv preprint arXiv:2007.01162, 2020
Ditto: Fair and Robust Federated Learning Through Personalization
T Li, S Hu, A Beirami, V Smith
arXiv preprint arXiv:2012.04221, 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
Heterogeneity for the Win: One-Shot Federated Clustering
DK Dennis, T Li, V Smith
arXiv preprint arXiv:2103.00697, 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
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
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
Ease. ML: A Lifecycle Management System for Machine Learning
L Aguilar, D Dao, S Gan, NM Gurel, N Hollenstein, J Jiang, B Karlas, ...
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
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