| Fedgroup: Efficient federated learning via decomposed similarity-based clustering M Duan, D Liu, X Ji, R Liu, L Liang, X Chen, Y Tan 2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications …, 2021 | 137* | 2021 |
| Flexible clustered federated learning for client-level data distribution shift M Duan, D Liu, X Ji, Y Wu, L Liang, X Chen, Y Tan, A Ren IEEE Transactions on Parallel and Distributed Systems 33 (11), 2661-2674, 2021 | 109 | 2021 |
| FedRich: Towards efficient federated learning for heterogeneous clients using heuristic scheduling H Yang, W Xi, Z Wang, Y Shen, X Ji, C Sun, J Zhao Information Sciences 645, 119360, 2023 | 7 | 2023 |
| FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning X Ji, Z Zhu, W Xi, O Gadyatskaya, Z Song, Y Cai, Y Liu Proceedings of the AAAI Conference on Artificial Intelligence 38 (11), 12830 …, 2024 | 5 | 2024 |
| HCAR: Human continuous activity recognition using latent structure features K Zhao, X Yang, W Xi, Z Jiang, X Wang, Z Wang, X Ji, Z Yang, J Zhao Transactions on Emerging Telecommunications Technologies 32 (6), e3807, 2021 | 3 | 2021 |
| Extreme Learning Machine Based Diagnosis Models for Erythemato-Squamous Diseases J Xie, X Ji, M Wang International Conference on Health Information Science, 61-74, 2018 | 2 | 2018 |
| Meta Generative Flow Networks with personalization for task-specific adaptation X Ji, X Zhang, W Xi, H Wang, O Gadyatskaya, Y Li Information Sciences 672, 120569, 2024 | | 2024 |
| Muse: A Trustworthy Vertical Federated Feature Selection Framework X Ji, W Xi, O Gadyatskaya, C Wang, F Zhao, Z Mao Available at SSRN 4164783, 0 | | |
| Federated Learning with Heterogeneous Label Noise: A Dual Structure Approach X Ji, Z Zhu, W Xi, O Gadyatskaya, Z Di, Y Liu | | |
