| 2026 | SIGCOMM | Accelerating Resilient Geo-Distributed LLM Training via Photonic-Computing-Assisted Vandermonde-Orthogonal Multiplexing. | Geyang Wang, Yuxin Wang, Meihan Wu, Weichi Wu, Paul R. Prucnal, Lian-Kuan Chen |
| 2025 | ICCV | FedVLA: Federated Vision-Language-Action Learning with Dual Gating Mixture-of-Experts for Robotic Manipulation. | Cui Miao, Tao Chang, Meihan Wu, Hongbin Xu, Chun Li, Ming Li, Xiaodong Wang |
| 2025 | ICCV | EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients. | Meihan Wu, Tao Chang, Cui Miao, Jie Zhou, Chun Li, Xiangyu Xu, Ming Li, Xiaodong Wang |
| 2024 | IWQoS | FedEKT: Ensemble Knowledge Transfer for Model-Heterogeneous Federated Learning. | Meihan Wu, Li Li, Tao Chang, Peng Qiao, Cui Miao, Jie Zhou, Jingnan Wang, Xiaodong Wang |
| 2024 | IWQoS | PFed-DBA: Distribution Bias Aware Personalized Federated Learning for Data Heterogeneity. | Meihan Wu, Li Li, Tao Chang, Jie Zhou, Eric Rigall, Cui Miao, Xiaodong Wang, Chengzhong Xu |
| 2023 | SECON | FedHybrid: Hierarchical Hybrid Training for High-Performance Federated Learning. | Tao Chang, Li Li, Meihan Wu, Wei Yu, Xiaodong Wang |
| 2022 | CIKM | FedCDR: Federated Cross-Domain Recommendation for Privacy-Preserving Rating Prediction. | Meihan Wu, Li Li, Chang Tao, Eric Rigall, Xiaodong Wang, Cheng-Zhong Xu |