| A learning-based incentive mechanism for federated learning Y Zhan, P Li, Z Qu, D Zeng, S Guo IEEE Internet of Things Journal 7 (7), 6360-6368, 2020 | 273 | 2020 |
| Cooperative caching for multiple bitrate videos in small cell edges Z Qu, B Ye, B Tang, S Guo, S Lu, W Zhuang IEEE Transactions on Mobile Computing 19 (2), 288-299, 2019 | 31 | 2019 |
| Adaptive federated learning on non-iid data with resource constraint J Zhang, S Guo, Z Qu, D Zeng, Y Zhan, Q Liu, R Akerkar IEEE Transactions on Computers 71 (7), 1655-1667, 2021 | 30 | 2021 |
| Edge learning: The enabling technology for distributed big data analytics in the edge J Zhang, Z Qu, C Chen, H Wang, Y Zhan, B Ye, S Guo ACM Computing Surveys (CSUR) 54 (7), 1-36, 2021 | 20 | 2021 |
| On-device learning systems for edge intelligence: A software and hardware synergy perspective Q Zhou, Z Qu, S Guo, B Luo, J Guo, Z Xu, R Akerkar IEEE Internet of Things Journal 8 (15), 11916-11934, 2021 | 19 | 2021 |
| Incentive mechanism design for federated learning: Challenges and opportunities Y Zhan, P Li, S Guo, Z Qu IEEE Network 35 (4), 310-317, 2021 | 16 | 2021 |
| Partial synchronization to accelerate federated learning over relay-assisted edge networks Z Qu, S Guo, H Wang, B Ye, Y Wang, AY Zomaya, B Tang IEEE Transactions on Mobile Computing 21 (12), 4502-4516, 2021 | 15 | 2021 |
| Petrel: Heterogeneity-aware distributed deep learning via hybrid synchronization Q Zhou, S Guo, Z Qu, P Li, L Li, M Guo, K Wang IEEE Transactions on Parallel and Distributed Systems 32 (5), 1030-1043, 2020 | 15 | 2020 |
| Physical-layer arithmetic for federated learning in uplink MU-MIMO enabled wireless networks T Huang, B Ye, Z Qu, B Tang, L Xie, S Lu IEEE INFOCOM 2020-IEEE Conference on Computer Communications, 1221-1230, 2020 | 14 | 2020 |
| A comprehensive survey on training acceleration for large machine learning models in IoT H Wang, Z Qu, Q Zhou, H Zhang, B Luo, W Xu, S Guo, R Li IEEE Internet of Things Journal 9 (2), 939-963, 2021 | 12 | 2021 |
| Octo: INT8 Training with Loss-aware Compensation and Backward Quantization for Tiny On-device Learning. Q Zhou, S Guo, Z Qu, J Guo, Z Xu, J Zhang, T Guo, B Luo, J Zhou USENIX Annual Technical Conference, 177-191, 2021 | 10 | 2021 |
| Error-compensated sparsification for communication-efficient decentralized training in edge environment H Wang, S Guo, Z Qu, R Li, Z Liu IEEE Transactions on Parallel and Distributed Systems 33 (1), 14-25, 2021 | 10 | 2021 |
| Intermittent pulling with local compensation for communication-efficient distributed learning H Wang, Z Qu, S Guo, X Gao, R Li, B Ye IEEE Transactions on Emerging Topics in Computing 10 (2), 779-791, 2020 | 6 | 2020 |
| Adaptive vertical federated learning on unbalanced features J Zhang, S Guo, Z Qu, D Zeng, H Wang, Q Liu, AY Zomaya IEEE Transactions on Parallel and Distributed Systems 33 (12), 4006-4018, 2022 | 5 | 2022 |
| Intermittent pulling with local compensation for communication-efficient federated learning H Wang, Z Qu, S Guo, X Gao, R Li, B Ye arXiv preprint arXiv:2001.08277, 2020 | 5 | 2020 |
| LOSP: Overlap synchronization parallel with local compensation for fast distributed training H Wang, Z Qu, S Guo, N Wang, R Li, W Zhuang IEEE Journal on Selected Areas in Communications 39 (8), 2541-2557, 2021 | 4 | 2021 |
| On the convergence of quantized parallel restarted SGD for serverless learning F Wu, S He, Y Yang, H Wang, Z Qu, S Guo arXiv preprint arXiv:2004.09125, 2020 | 4 | 2020 |
| Edge Learning for Distributed Big Data Analytics: Theory, Algorithms, and System Design S Guo, Z Qu Cambridge University Press, 2022 | 3 | 2022 |
| Scheduling coflows of multi-stage jobs under network resource constraints Y Zeng, B Ye, B Tang, S Guo, Z Qu Computer Networks 184, 107686, 2021 | 3 | 2021 |
| Joint service placement and computation offloading in mobile edge computing: an auction-based approach L Zhang, Z Qu, B Ye, B Tang 2020 IEEE 26th International Conference on Parallel and Distributed Systems …, 2020 | 3 | 2020 |
