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PFed-DBA: Distribution Bias Aware Personalized Federated Learning for Data Heterogeneity Conference paper
Meihan Wu, Li Li, Tao Chang, Jie Zhou, Cui Miao, Xiaodong Wang, ChengZhong Xu, Rigall, Eric. PFed-DBA: Distribution Bias Aware Personalized Federated Learning for Data Heterogeneity[C]:Institute of Electrical and Electronics Engineers Inc., 2024, 202971.
Authors:  Meihan Wu;  Li Li;  Tao Chang;  Jie Zhou;  Cui Miao; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0 | Submit date：2024/08/29
Contrastive Learning  Data Heterogeneity  Ersonalized Federated Learning  Representation Learning  
FedEKT: Ensemble Knowledge Transfer for Model-Heterogeneous Federated Learning Conference paper
Meihan Wu, Li Li, Tao Chang, Peng Qiao, Cui Miao, Jie Zhou, Jingnan Wang, Xiaodong Zhang. FedEKT: Ensemble Knowledge Transfer for Model-Heterogeneous Federated Learning[C]:Institute of Electrical and Electronics Engineers Inc., 2024, 202971.
Authors:  Meihan Wu;  Li Li;  Tao Chang;  Peng Qiao;  Cui Miao; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0 | Submit date：2024/08/29
Federated Learning  Knowledge Transfer  Model Heterogeneity  
FedHybrid: Hierarchical Hybrid Training for High-Performance Federated Learning Conference paper
Tao Chang, Li Li, Meihan Wu, Wei Yu, Xiaodong Wang. FedHybrid: Hierarchical Hybrid Training for High-Performance Federated Learning[C], 2023.
Authors:  Tao Chang;  Li Li;  Meihan Wu;  Wei Yu;  Xiaodong Wang
Favorite | TC[WOS]:0 TC[Scopus]:0 | Submit date：2023/12/14
GraphCS: Graph-based Client Selection for Heterogeneity in Federated Learning Journal article
Tao Chang, Li Li, Meihan Wu, Xiaodong Wang, ChengZhong Xu, Wei Yu. GraphCS: Graph-based Client Selection for Heterogeneity in Federated Learning[J]. Journal of Parallel and Distributed Computing, 2023, 177, 131-143.
Authors:  Tao Chang;  Li Li;  Meihan Wu;  Xiaodong Wang;  ChengZhong Xu; et al.
Favorite | TC[WOS]:3 TC[Scopus]:7  IF:3.4/3.4 | Submit date：2023/08/28
Federated Learning  Client Selection  Heterogeneity  
FedCDR: Federated Cross-Domain Recommendation for Privacy-Preserving Rating Prediction Conference paper
Meihan Wu, Li Li, Tao Chang, Eric Rigall, Xiaodong Wang, ChengZhong Xu. FedCDR: Federated Cross-Domain Recommendation for Privacy-Preserving Rating Prediction[C]. Mohammad Al Hasan, Li Xiong, New York, NY, United States:Association for Computing Machinery, 2022, 2179–2188.
Authors:  Meihan Wu;  Li Li;  Tao Chang;  Eric Rigall;  Xiaodong Wang; et al.
Favorite | TC[WOS]:11 TC[Scopus]:19 | Submit date：2022/08/30
Personalized Federated Learning  Cross-domain Recommendation  Cold-start Problem  Rating Prediction