[51] Gaganpreet Sharma. Pros and cons of different sampling techniques. International journal of applied research, 3(7):749-752, 2017.
[52] Guangyuan Shen, Dehong Gao, DuanXiao Song, Xukai Zhou, Shirui Pan, Wei Lou, Fang Zhou, et al. Fast heterogeneous federated learning with hybrid client selection. arXiv preprint arXiv:2208.05135, 2022.
[53] Chengshuai Shi and Cong Shen. Federated multi-armed bandits. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 9603–9611, 2021.
[54] Sebastian U Stich, Anant Raj, and Martin Jaggi. Safe adaptive importance sampling. arXiv preprint arXiv:1711.02637, 2017.
[55] Canh T. Dinh, Nguyen Tran, and Josh Nguyen. Personalized federated learning with moreau envelopes. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 21394-21405. Curran Associates, Inc., 2020.
[56] Steven K Thompson. Adaptive cluster sampling. Journal of the American Statistical Association, 85(412):1050-1059, 1990.
[57] Alexander Tyurin and Peter Richtárik. A computation and communication efficient method for distributed nonconvex problems in the partial participation setting. arXiv preprint arXiv:2205.15580, 2022.
[58] Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H Brendan McMahan, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, et al. A field guide to federated optimization. arXiv preprint arXiv:2107.06917, 2021.
[59] Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H Vincent Poor. Tackling the objective inconsistency problem in heterogeneous federated optimization. arXiv preprint arXiv:2007.07481, 2020.
[60] Shiqiang Wang and Mingyue Ji. A unified analysis of federated learning with arbitrary client participation. arXiv preprint arXiv:2205.13648, 2022.
[61] Joel Wolfrath, Nikhil Sreekumar, Dhruv Kumar, Yuanli Wang, and Abhishek Chandra. Haccs: Heterogeneity-aware clustered client selection for accelerated federated learning. In 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pages 985–995, 2022.
[62] Wenchao Xia, Tony QS Quek, Kun Guo, Wanli Wen, Howard H Yang, and Hongbo Zhu. Multiarmed bandit-based client scheduling for federated learning. IEEE Transactions on Wireless Communications, 19(11):7108-7123, 2020.
[63] Han Xie, Jing Ma, Li Xiong, and Carl Yang. Federated graph classification over non-iid graphs. Advances in neural information processing systems, 34:18839-18852, 2021.
[64] Xiaohui Xu, Sijing Duan, Jinrui Zhang, Yunzhen Luo, and Deyu Zhang. Optimizing federated learning on device heterogeneity with a sampling strategy. In 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS), pages 1-10, 2021.
[65] Haibo Yang, Minghong Fang, and Jia Liu. Achieving linear speedup with partial worker participation in non-iid federated learning. arXiv preprint arXiv:2101.11203, 2021.
[66] Hao Yu, Sen Yang, and Shenghuo Zhu. Parallel restarted sgd with faster convergence and less communication: Demystifying why model averaging works for deep learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 5693-5700, 2019.
[67] Hangjia Zhang, Zhijun Xie, Roozbeh Zarei, Tao Wu, and Kewei Chen. Adaptive client selection in resource constrained federated learning systems: A deep reinforcement learning approach. IEEE Access, 9:98423-98432, 2021.
[68] Xinwei Zhang, Mingyi Hong, Sairaj Dhople, Wotao Yin, and Yang Liu. Fedpd: A federated learning framework with optimal rates and adaptivity to non-iid data. arXiv preprint arXiv:2005.11418, 2020.