Ditto: Fair and robust federated learning through personalization T Li, S Hu, A Beirami, V Smith International conference on machine learning, 6357-6368, 2021 | 673 | 2021 |
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 | 126 | 2019 |
On privacy and personalization in cross-silo federated learning K Liu, S Hu, SZ Wu, V Smith Advances in neural information processing systems 35, 5925-5940, 2022 | 36 | 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 | 25 | 2022 |
Fair federated learning via bounded group loss S Hu, ZS Wu, V Smith 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 140-160, 2024 | 19 | 2024 |
Federated multi-task learning for competing constraints T Li, S Hu, A Beirami, V Smith arXiv preprint arXiv:2012.04221, 2020 | 14 | 2020 |
Private multi-task learning: Formulation and applications to federated learning S Hu, ZS Wu, V Smith arXiv preprint arXiv:2108.12978, 2021 | 12 | 2021 |
Attacking LLM Watermarks by Exploiting Their Strengths Q Pang, S Hu, W Zheng, V Smith arXiv preprint arXiv:2402.16187, 2024 | 1 | 2024 |
Federated Learning as a Network Effects Game S Hu, DD Ngo, S Zheng, V Smith, ZS Wu arXiv preprint arXiv:2302.08533, 2023 | 1 | 2023 |
Privacy Amplification for the Gaussian Mechanism via Bounded Support S Hu, S Mahloujifar, V Smith, K Chaudhuri, C Guo arXiv preprint arXiv:2403.05598, 2024 | | 2024 |
PRIVATE MULTI-TASK LEARNING: FORMULATION AND METHODS S Hu, ZS Wu, V Smith | | |