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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
43622020
Federated optimization in heterogeneous networks
T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith
Conference on Machine Learning and Systems (MLSys), 2018
41572018
Leaf: A benchmark for federated settings
S Caldas, SMK Duddu, P Wu, T Li, J Konečnı, HB McMahan, V Smith, ...
NeurIPS 2019 Workshop on Federated Learning for Data Privacy and Confidentiality, 2018
12222018
Fair resource allocation in federated learning
T Li, M Sanjabi, A Beirami, V Smith
International Conference on Learning Representations (ICLR), 2019
7682019
Ditto: Fair and Robust Federated Learning Through Personalization
T Li, S Hu, A Beirami, V Smith
International Conference on Machine Learning (ICML), 2020
6572020
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
3082021
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
1502019
Tilted empirical risk minimization
T Li*, A Beirami*, M Sanjabi, V Smith
International Conference on Learning Representations (ICLR), 2020
1212020
Heterogeneity for the win: One-shot federated clustering
DK Dennis, T Li, V Smith
International Conference on Machine Learning, 2611-2620, 2021
1162021
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
962018
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
802020
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, 2022
732022
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
702021
Motley: Benchmarking heterogeneity and personalization in federated learning
S Wu, T Li, Z Charles, Y Xiao, Z Liu, Z Xu, V Smith
NeurIPS 2022 Workshop on Federated Learning: Recent Advances and New Challenges, 2022
342022
Ease. ml: a lifecycle management system for MLDev and MLOps
L Aguilar Melgar, D Dao, S Gan, NM Gürel, N Hollenstein, J Jiang, ...
Proceedings of the Annual Conference on Innovative Data Systems Research …, 2021
32*2021
Private adaptive optimization with side information
T Li, M Zaheer, S Reddi, V Smith
International Conference on Machine Learning, 13086-13105, 2022
312022
Enhancing the privacy of federated learning with sketching
Z Liu, T Li, V Smith, V Sekar
arXiv preprint arXiv:1911.01812, 2019
302019
On tilted losses in machine learning: Theory and applications
T Li*, A Beirami*, M Sanjabi, V Smith
Journal of Machine Learning Research (JMLR), 2021
262021
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
232021
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
142020
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Articles 1–20