Federated unsupervised representation learning F Zhang, K Kuang, L Chen, Z You, T Shen, J Xiao, Y Zhang, C Wu, F Wu, ... Frontiers of Information Technology & Electronic Engineering 24 (8), 1181-1193, 2023 | 112 | 2023 |
Federated mutual learning T Shen, J Zhang, X Jia, F Zhang, G Huang, P Zhou, K Kuang, F Wu, C Wu arXiv preprint arXiv:2006.16765, 2020 | 101 | 2020 |
A graph-based power flow method for balanced distribution systems T Shen, Y Li, J Xiang Energies 11 (3), 511, 2018 | 83 | 2018 |
Edge-cloud polarization and collaboration: A comprehensive survey for ai J Yao, S Zhang, Y Yao, F Wang, J Ma, J Zhang, Y Chu, L Ji, K Jia, T Shen, ... IEEE Transactions on Knowledge and Data Engineering 35 (7), 6866-6886, 2022 | 54 | 2022 |
Federated graph learning--a position paper H Zhang, T Shen, F Wu, M Yin, H Yang, C Wu arXiv preprint arXiv:2105.11099, 2021 | 39 | 2021 |
Duet: A tuning-free device-cloud collaborative parameters generation framework for efficient device model generalization Z Lv, W Zhang, S Zhang, K Kuang, F Wang, Y Wang, Z Chen, T Shen, ... Proceedings of the ACM Web Conference 2023, 3077-3085, 2023 | 29 | 2023 |
FedDTG: Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networks Z Zhang, T Shen, J Zhang, C Wu arXiv preprint arXiv:2201.03169, 2022 | 8 | 2022 |
Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models D Zhu, Z Sun, Z Li, T Shen, K Yan, S Ding, K Kuang, C Wu arXiv preprint arXiv:2402.12048, 2024 | 1 | 2024 |
FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning T Shen, Z Li, Y Li, Z Zhao, F Zhang, S Zhang, K Kuang, C Wu, F Wu | | |