Follow
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
16712020
Federated optimization in heterogeneous networks
T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith
arXiv preprint arXiv:1812.06127, 2018
13022018
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
5462018
Fair resource allocation in federated learning
T Li, M Sanjabi, A Beirami, V Smith
arXiv preprint arXiv:1905.10497, 2019
3622019
Ditto: Fair and Robust Federated Learning Through Personalization
T Li, S Hu, A Beirami, V Smith
arXiv preprint arXiv:2012.04221, 2020
1372020
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
952021
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
672018
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
582019
Tilted empirical risk minimization
T Li, A Beirami, M Sanjabi, V Smith
arXiv preprint arXiv:2007.01162, 2020
412020
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
342020
Heterogeneity for the win: One-shot federated clustering
DK Dennis, T Li, V Smith
International Conference on Machine Learning, 2611-2620, 2021
242021
Enhancing the privacy of federated learning with sketching
Z Liu, T Li, V Smith, V Sekar
arXiv preprint arXiv:1911.01812, 2019
172019
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
142021
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
82017
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
52021
On tilted losses in machine learning: Theory and applications
T Li, A Beirami, M Sanjabi, V Smith
arXiv preprint arXiv:2109.06141, 2021
52021
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
52021
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
52020
Private adaptive optimization with side information
T Li, M Zaheer, S Reddi, V Smith
International Conference on Machine Learning, 13086-13105, 2022
12022
The system can't perform the operation now. Try again later.
Articles 1–20