Seguir
Tian Li
Título
Citado por
Citado por
Año
Federated learning: Challenges, methods, and future directions
T Li, AK Sahu, A Talwalkar, V Smith
IEEE signal processing magazine 37 (3), 50-60, 2020
42462020
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
39832018
Leaf: A benchmark for federated settings
S Caldas, SMK Duddu, P Wu, T Li, J Konečný, HB McMahan, V Smith, ...
NeurIPS 2019 Workshop on Federated Learning for Data Privacy and Confidentiality, 2018
12012018
Fair resource allocation in federated learning
T Li, M Sanjabi, A Beirami, V Smith
International Conference on Learning Representations (ICLR), 2019
7552019
Ditto: Fair and Robust Federated Learning Through Personalization
T Li, S Hu, A Beirami, V Smith
International Conference on Machine Learning (ICML), 2020
6262020
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
3002021
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
1422019
Tilted empirical risk minimization
T Li*, A Beirami*, M Sanjabi, V Smith
International Conference on Learning Representations (ICLR), 2020
1182020
Heterogeneity for the win: One-shot federated clustering
DK Dennis, T Li, V Smith
International Conference on Machine Learning, 2611-2620, 2021
1132021
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
952018
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
782020
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
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
682022
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
322022
Private adaptive optimization with side information
T Li, M Zaheer, S Reddi, V Smith
International Conference on Machine Learning, 13086-13105, 2022
302022
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
30*2021
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
252021
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
222021
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
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20