Tian Li
Title
Cited by
Cited by
Year
Federated learning: Challenges, methods, and future directions
T Li, AK Sahu, A Talwalkar, V Smith
IEEE Signal Processing Magazine 37 (3), 50-60, 2020
7712020
Federated optimization in heterogeneous networks
T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith
arXiv preprint arXiv:1812.06127, 2018
5622018
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
2572018
Fair resource allocation in federated learning
T Li, M Sanjabi, A Beirami, V Smith
arXiv preprint arXiv:1905.10497, 2019
1802019
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
462018
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
332019
Ditto: Fair and Robust Federated Learning Through Personalization
T Li, S Hu, A Beirami, V Smith
arXiv preprint arXiv:2012.04221, 2020
14*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
122020
Enhancing the privacy of federated learning with sketching
Z Liu, T Li, V Smith, V Sekar
arXiv preprint arXiv:1911.01812, 2019
112019
Tilted empirical risk minimization
T Li, A Beirami, M Sanjabi, V Smith
arXiv preprint arXiv:2007.01162, 2020
102020
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
82017
Heterogeneity for the Win: One-Shot Federated Clustering
DK Dennis, T Li, V Smith
arXiv preprint arXiv:2103.00697, 2021
32021
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
22021
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
22021
Diverse Client Selection for Federated Learning: Submodularity and Convergence Analysis
R Balakrishnan, T Li, T Zhou, N Himayat, V Smith, J Bilmes
International Workshop on Federated Learning for User Privacy and Data …, 2020
12020
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
12020
Sketchlib: Enabling efficient sketch-based monitoring on programmable switches
H Namkung, Z Liu, D Kim, V Sekar, P Steenkiste, G Liu, A Li, C Canel, ...
NSDI, 0
1
On Tilted Losses in Machine Learning: Theory and Applications
T Li, A Beirami, M Sanjabi, V Smith
arXiv preprint arXiv:2109.06141, 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
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