| Ditto: Fair and robust federated learning through personalization T Li, S Hu, A Beirami, V Smith International Conference on Machine Learning, 6357-6368, 2021 | 112 | 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 | 69 | 2019 |
| Federated multi-task learning for competing constraints T Li, S Hu, A Beirami, V Smith | 11 | 2020 |
| Private multi-task learning: Formulation and applications to federated learning S Hu, ZS Wu, V Smith arXiv preprint arXiv:2108.12978, 2021 | 3 | 2021 |
| 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 | | |
Virginia SmithCarnegie Mellon UniversityVerified email at cmu.edu
