| Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning H Yang, M Fang, J Liu Proceedings of ICLR, 2021 | 287 | 2021 |
| Anarchic Federated Learning H Yang, X Zhang, P Khanduri, J Liu International Conference on Machine Learning, 2022 | 75 | 2022 |
| Stem: A stochastic two-sided momentum algorithm achieving near-optimal sample and communication complexities for federated learning P Khanduri, P Sharma, H Yang, M Hong, J Liu, K Rajawat, P Varshney Advances in Neural Information Processing Systems 34, 6050-6061, 2021 | 71 | 2021 |
| Byzantine-resilient stochastic gradient descent for distributed learning: A lipschitz-inspired coordinate-wise median approach H Yang, X Zhang, M Fang, J Liu 2019 IEEE 58th Conference on Decision and Control (CDC), 5832-5837, 2019 | 50 | 2019 |
| Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control H Yang, P Qiu, J Liu, A Yener IEEE ISIT 2022, 2022 | 22 | 2022 |
| CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning H Yang, J Liu, ES Bentley IEEE/IFIP WiOpt 2021, 2021 | 21 | 2021 |
| CHARLES: Channel-Quality-Adaptive Over-the-Air Federated Learning over Wireless Networks J Mao, H Yang, P Qiu, J Liu, A Yener IEEE SPAWC 2022, 2022 | 16 | 2022 |
| Net-fleet: Achieving linear convergence speedup for fully decentralized federated learning with heterogeneous data X Zhang, M Fang, Z Liu, H Yang, J Liu, Z Zhu Proc. ACM MobiHoc 2022, 2022 | 15 | 2022 |
| Sagda: Achieving O (ϵ− 2) communication complexity in federated min-max learning H Yang, Z Liu, X Zhang, J Liu NeurIPS 2022, 2022 | 13* | 2022 |
| Federated Multi-Objective Learning H Yang, Z Liu, J Liu, C Dong, M Momma NeurIPS 2023, 2023 | 8 | 2023 |
| Taming Fat-Tailed ("Heavier-Tailed'' with Potentially Infinite Variance) Noise in Federated Learning H Yang, P Qiu, J Liu NeurIPS 2022, 2022 | 8 | 2022 |
| Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach P Khanduri, H Yang, M Hong, J Liu, H Wai, S Liu Proc. ICLR, 2022 | 6 | 2022 |
| On the Efficacy of Server-Aided Federated Learning against Partial Client Participation H Yang, P Qiu, P Khanduri, J Liu | 2 | 2022 |
| Adversarial Attacks to Multi-Modal Models Z Dou, X Hu, H Yang, Z Liu, M Fang arXiv preprint arXiv:2409.06793, 2024 | 1 | 2024 |
| Adaptive multi-hierarchical signSGD for communication-efficient distributed optimization H Yang, X Zhang, M Fang, J Liu 2020 IEEE 21st International Workshop on Signal Processing Advances in …, 2020 | 1 | 2020 |
| Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning? P Ju, H Yang, J Liu, Y Liang, N Shroff Proceedings of the Twenty-fifth International Symposium on Theory …, 2024 | | 2024 |
| Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization Z Li, B Ying, Z Liu, H Yang arXiv preprint arXiv:2405.15861, 2024 | | 2024 |
| Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation H Yang, P Qiu, P Khanduri, M Fang, J Liu ICML 2024, 2024 | | 2024 |
| Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning T Zhou, FNU Hairi, H Yang, J Liu, T Tong, F Yang, M Momma, Y Gao ICML 2024, 2024 | | 2024 |
| STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning Z Liu, C Dong, M Momma, S Shao, S Xu, H Yang, J Liu | | 2023 |
Jia (Kevin) LiuAssociate Professor of Electrical and Computer Engineering, The Ohio State UniversityVerified email at ece.osu.edu
