The following publications are possibly variants of this publication:
- Analyzing the Convergence of Federated Learning with Biased Client ParticipationLei Tan, Miao Hu, Yipeng Zhou, Di Wu 0001. adma 2023: 423-439 [doi]
- Incentive-Aware Autonomous Client Participation in Federated LearningMiao Hu, Di Wu 0001, Yipeng Zhou, Xu Chen 0004, Min Chen 0003. tpds, 33(10):2612-2627, 2022. [doi]
- Anchor Sampling for Federated Learning with Partial Client ParticipationFeijie Wu, Song Guo 0001, Zhihao Qu, Shiqi He, Ziming Liu, Jing Gao 0004. icml 2023: 37379-37416 [doi]
- AutoFL: A Bayesian Game Approach for Autonomous Client Participation in Federated Edge LearningMiao Hu, Wenzhuo Yang, Zhenxiao Luo, Xuezheng Liu, Yipeng Zhou, Xu Chen 0004, Di Wu 0001. tmc, 23(1):194-208, January 2024. [doi]
- Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client ParticipationBing Luo, Yutong Feng, Shiqiang Wang 0001, Jianwei Huang 0001, Leandros Tassiulas. icdcs 2023: 545-555 [doi]
- Efficient Federated Learning with Self-Regulating ClientsZahidur Talukder, Mohammad A. Islam 0001. sigmetrics, 50(4):23-25, April 2023. [doi]