| Personalized federated learning with differential privacy R Hu, Y Guo, H Li, Q Pei, Y Gong IEEE Internet of Things Journal 7 (10), 9530-9539, 2020 | 237 | 2020 |
| DP-ADMM: ADMM-based distributed learning with differential privacy Z Huang, R Hu, Y Guo, E Chan-Tin, Y Gong IEEE Transactions on Information Forensics and Security 15, 1002-1012, 2019 | 186 | 2019 |
| Trading data for learning: Incentive mechanism for on-device federated learning R Hu, Y Gong GLOBECOM 2020-2020 IEEE Global Communications Conference, 1-6, 2020 | 67 | 2020 |
| Federated learning with sparsification-amplified privacy and adaptive optimization R Hu, Y Gong, Y Guo arXiv preprint arXiv:2008.01558, 2020 | 39 | 2020 |
| Hybrid local SGD for federated learning with heterogeneous communications Y Guo, Y Sun, R Hu, Y Gong International Conference on Learning Representations, 2022 | 36 | 2022 |
| Targeted poisoning attacks on social recommender systems R Hu, Y Guo, M Pan, Y Gong 2019 IEEE Global Communications Conference (GLOBECOM), 1-6, 2019 | 35 | 2019 |
| Federated learning with sparsified model perturbation: Improving accuracy under client-level differential privacy R Hu, Y Guo, Y Gong IEEE Transactions on Mobile Computing, 2023 | 29 | 2023 |
| Concentrated differentially private federated learning with performance analysis R Hu, Y Guo, Y Gong IEEE Open Journal of the Computer Society 2, 276-289, 2021 | 23 | 2021 |
| Differentially private federated learning for resource-constrained Internet of Things R Hu, Y Guo, EP Ratazzi, Y Gong arXiv preprint arXiv:2003.12705, 2020 | 21 | 2020 |
| CPFed: Communication-efficient and privacy-preserving federated learning R Hu, Y Gong, Y Guo arXiv preprint arXiv:2003.13761, 2020 | 21 | 2020 |
| Certified robustness of graph classification against topology attack with randomized smoothing Z Gao, R Hu, Y Gong GLOBECOM 2020-2020 IEEE Global Communications Conference, 1-6, 2020 | 16 | 2020 |
| Privacy-preserving personalized federated learning R Hu, Y Guo, H Li, Q Pei, Y Gong ICC 2020-2020 IEEE International Conference on Communications (ICC), 1-6, 2020 | 15 | 2020 |
| Concentrated differentially private and utility preserving federated learning R Hu, Y Guo, Y Gong arXiv preprint arXiv:2003.13761, 2020 | 11 | 2020 |
| Exploring the Efficacy of Data-Decoupled Federated Learning for Image Classification and Medical Imaging Analysis MJ Khan, OT Tawose, R Hu, D Zhao International Workshop on Federated Learning for Distributed Data Mining, 2023 | 9 | 2023 |
| Byzantine-robust federated learning with variance reduction and differential privacy Z Zhang, R Hu 2023 IEEE Conference on Communications and Network Security (CNS), 1-9, 2023 | 7 | 2023 |
| Energy-efficient distributed machine learning at wireless edge with device-to-device communication R Hu, Y Guo, Y Gong ICC 2022-IEEE International Conference on Communications, 5208-5213, 2022 | 4 | 2022 |
| Agent-level differentially private federated learning via compressed model perturbation Y Guo, R Hu, Y Gong 2022 IEEE Conference on Communications and Network Security (CNS), 127-135, 2022 | 2 | 2022 |
| Comparative Evaluation of Data Decoupling Techniques for Federated Machine Learning with Database as a Service MJ Khan, R Hu, M Sadoghi, D Zhao arXiv preprint arXiv:2303.08371, 2023 | 1 | 2023 |
| Breaking the Privacy Paradox: Pushing AI to the Edge with Provable Guarantees R Hu The University of Texas at San Antonio, 2022 | 1 | 2022 |
| Comparative Evaluation of Data Decoupling Techniques for Federated Machine Learning with Database as a Service M Jahanzeb Khan, R Hu, M Sadoghi, D Zhao arXiv e-prints, arXiv: 2303.08371, 2023 | | 2023 |
Yanmin GongUniversity of Texas at San AntonioVerified email at utsa.edu
