The following publications are possibly variants of this publication:
- Bio-Inspired Dual-Network Model to Tackle Statistical Heterogeneity in Federated LearningAdnan Ahmad, Vinh Loi Chau, Antonio Robles-Kelly, Shang Gao 0003, Longxiang Gao, Lianhua Chi, Wei Luo 0001. ijcnn 2023: 1-8 [doi]
- A Hessian-Based Federated Learning Approach to Tackle Statistical HeterogeneityAdnan Ahmad, Wei Luo 0001, Antonio Robles-Kelly. adma 2023: 408-422 [doi]
- Tackling Privacy Heterogeneity in Federated LearningRuichen Xu, Ying Jun Angela Zhang, Jianwei Huang 0001. wiopt 2023: 326-333 [doi]
- Heterogeneity-Aware Federated Learning with Adaptive Client Selection and Gradient CompressionZhida Jiang, Yang Xu 0020, Hongli Xu, Zhiyuan Wang, Chen Qian 0001. infocom 2023: 1-10 [doi]
- RingSFL: An Adaptive Split Federated Learning Towards Taming Client HeterogeneityJinglong Shen, Nan Cheng, Xiucheng Wang, Feng Lyu 0001, Wenchao Xu 0001, Zhi Liu 0002, Khalid Aldubaikhy, Xuemin Shen. tmc, 23(5):5462-5478, May 2024. [doi]
- Tackling Data Heterogeneity in Federated Learning with Class PrototypesYutong Dai 0002, Zeyuan Chen, Junnan Li 0001, Shelby Heinecke, Lichao Sun 0001, Ran Xu. AAAI 2023: 7314-7322 [doi]