Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning H Yang, M Fang, J Liu Proceedings of ICLR, 2021 | 241 | 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 | 64 | 2021 |
Anarchic Federated Learning H Yang, X Zhang, P Khanduri, J Liu International Conference on Machine Learning, 2022 | 63 | 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 | 42 | 2019 |
CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning H Yang, J Liu, ES Bentley IEEE/IFIP WiOpt 2021, 2021 | 18 | 2021 |
Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control H Yang, P Qiu, J Liu, A Yener IEEE ISIT 2022, 2022 | 17 | 2022 |
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 |
Sagda: Achieving O (ϵ− 2) communication complexity in federated min-max learning H Yang, Z Liu, X Zhang, J Liu NeurIPS 2022, 2022 | 10* | 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 | 9 | 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 | 6* | 2021 |
Taming Fat-Tailed ("Heavier-Tailed'' with Potentially Infinite Variance) Noise in Federated Learning H Yang, P Qiu, J Liu NeurIPS 2022, 2022 | 5 | 2022 |
Federated Multi-Objective Learning H Yang, Z Liu, J Liu, C Dong, M Momma NeurIPS 2023, 2023 | 2 | 2023 |
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 |
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 |
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 |