| 2026 | KDD | Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Masking. | Xingchen Wang, Feijie Wu, Chenglin Miao, Tianchun Li, Haoyu Hu, Qiming Cao, Jing Gao, Lu Su |
| 2025 | ICLR | Towards Federated RLHF with Aggregated Client Preference for LLMs. | Feijie Wu, Xiaoze Liu, Haoyu Wang, Xingchen Wang, Lu Su, Jing Gao |
| 2024 | EMNLP | SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generation. | Xiaoze Liu, Ting Sun, Tianyang Xu, Feijie Wu, Cunxiang Wang, Xiaoqian Wang, Jing Gao |
| 2024 | ICLR | Towards Poisoning Fair Representations. | Tianci Liu, Haoyu Wang, Feijie Wu, Hengtong Zhang, Pan Li, Lu Su, Jing Gao |
| 2024 | KDD | FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model. | Feijie Wu, Zitao Li, Yaliang Li, Bolin Ding, Jing Gao |
| 2024 | SENSYS | Towards Efficient Heterogeneous Multi-Modal Federated Learning with Hierarchical Knowledge Disentanglement. | Xingchen Wang, Haoyu Wang, Feijie Wu, Tianci Liu, Qiming Cao, Lu Su |
| 2023 | ICML | Anchor Sampling for Federated Learning with Partial Client Participation. | Feijie Wu, Song Guo, Zhihao Qu, Shiqi He, Ziming Liu, Jing Gao |
| 2023 | KDD | Macular: A Multi-Task Adversarial Framework for Cross-Lingual Natural Language Understanding. | Haoyu Wang, Yaqing Wang, Feijie Wu, Hongfei Xue, Jing Gao |
| 2022 | DAC | Sign bit is enough: a learning synchronization framework for multi-hop all-reduce with ultimate compression. | Feijie Wu, Shiqi He, Song Guo, Zhihao Qu, Haozhao Wang, Weihua Zhuang, Jie Zhang |