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Shengyuan Hu
Shengyuan Hu
Verified email at andrew.cmu.edu - Homepage
Title
Cited by
Cited by
Year
Ditto: Fair and robust federated learning through personalization
T Li, S Hu, A Beirami, V Smith
International Conference on Machine Learning, 6357-6368, 2021
1122021
A new defense against adversarial images: Turning a weakness into a strength
S Hu, T Yu, C Guo, WL Chao, KQ Weinberger
Advances in Neural Information Processing Systems 32, 2019
692019
Federated multi-task learning for competing constraints
T Li, S Hu, A Beirami, V Smith
112020
Private multi-task learning: Formulation and applications to federated learning
S Hu, ZS Wu, V Smith
arXiv preprint arXiv:2108.12978, 2021
32021
On Privacy and Personalization in Cross-Silo Federated Learning
Z Liu, S Hu, ZS Wu, V Smith
arXiv preprint arXiv:2206.07902, 2022
2022
FedSynth: Gradient Compression via Synthetic Data in Federated Learning
S Hu, J Goetz, K Malik, H Zhan, Z Liu, Y Liu
arXiv preprint arXiv:2204.01273, 2022
2022
Provably Fair Federated Learning via Bounded Group Loss
S Hu, ZS Wu, V Smith
arXiv preprint arXiv:2203.10190, 2022
2022
PRIVATE MULTI-TASK LEARNING: FORMULATION AND METHODS
S Hu, ZS Wu, V Smith
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