| Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning H Yang, M Fang, J Liu Proceedings of ICLR, 2021 | 225 | 2021 |
| 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 | 60 | 2021 |
| Anarchic Federated Learning H Yang, X Zhang, P Khanduri, J Liu International Conference on Machine Learning, 2022 | 58 | 2022 |
| 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 | 40 | 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 | 15 | 2022 |
| CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning H Yang, J Liu, ES Bentley IEEE/IFIP WiOpt 2021, 2021 | 15 | 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 | 11 | 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 | 8 | 2022 |
| Sagda: Achieving O (ϵ− 2) communication complexity in federated min-max learning H Yang, Z Liu, X Zhang, J Liu NeurIPS 2022, 2022 | 7* | 2022 |
| Taming Fat-Tailed ("Heavier-Tailed'' with Potentially Infinite Variance) Noise in Federated Learning H Yang, P Qiu, J Liu NeurIPS 2022, 2022 | 5 | 2022 |
| Decentralized learning for overparameterized problems: A multi-agent kernel approximation approach P Khanduri, H Yang, M Hong, J Liu, HT Wai, S Liu International Conference on Learning Representations, 2021 | 5 | 2021 |
| On the Efficacy of Server-Aided Federated Learning against Partial Client Participation H Yang, P Qiu, P Khanduri, J Liu | 2 | 2022 |
| 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 |
| Federated Multi-Objective Learning H Yang, Z Liu, J Liu, C Dong, M Momma NeurIPS 2023, 2023 | | 2023 |
| Understanding of Server-Assisted Federated Learning with Incomplete Client Participation H Yang, P Qiu, P Khanduri, M Fang, J Liu | | 2023 |
| Towards Efficient Federated Learning: Learning at Anytime, Anywhere and with Any Data H Yang The Ohio State University, 2023 | | 2023 |
| With a Little Help from My Friend: Server-Aided Federated Learning with Partial Client Participation H Yang, P Qiu, P Khanduri, J Liu Workshop on Federated Learning: Recent Advances and New Challenges (in …, 2022 | | 2022 |
Jia (Kevin) LiuAssistant Professor of Electrical and Computer Engineering, The Ohio State UniversityVerified email at ece.osu.edu
