The following publications are possibly variants of this publication:
- Adaptive Personalized Federated Learning for Non-IID Data with Continual Distribution ShiftSisi Chen, Weijie Liu, Xiaoxi Zhang, Hong Xu, Wanyu Lin, Xu Chen 0004. iwqos 2024: 1-6 [doi]
- FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID DataXin-Chun Li, De-Chuan Zhan. kdd 2021: 995-1005 [doi]
- Distribution-Regularized Federated Learning on Non-IID DataYansheng Wang, Yongxin Tong, Zimu Zhou, Ruisheng Zhang, Sinno Jialin Pan, Lixin Fan, Qiang Yang 0001. icde 2023: 2113-2125 [doi]
- Class-Wise Adaptive Self Distillation for Federated Learning on Non-IID Data (Student Abstract)Yuting He, Yiqiang Chen, Xiaodong Yang 0005, Yingwei Zhang, Bixiao Zeng. AAAI 2022: 12967-12968 [doi]
- Model Aggregation for Federated Learning Considering Non-IID and Imbalanced Data DistributionYuan Wang, Renuga Kanagavelu, Qingsong Wei, Yechao Yang, Yong Liu. brainles-ws 2023: 196-208 [doi]
- Feddaw: Dual Adaptive Weighted Federated Learning for Non-IID Medical DataLinan Ren, Kaixin Li, Ying An, Yuan Liu, Xianlai Chen. isbra 2024: 1-13 [doi]
- Overcoming Noisy Labels and Non-IID Data in Edge Federated LearningYang Xu 0020, Yunming Liao, Lun Wang, Hongli Xu, Zhida Jiang, Wuyang Zhang. tmc, 23(12):11406-11421, December 2024. [doi]