[13] Bill Yuchen Lin, Chaoyang He, Zihang Ze, Hulin Wang, Yufen Hua, Christophe Dupuy, Rahul Gupta, Mahdi Soltanolkotabi, Xiang Ren, and Salman Avestimehr. FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks. In NAACL, 2022.
[14] Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, and Salman Avestimehr. FedML: A Research Library and Benchmark for Federated Machine Learning. NeurIPS Federated Learning Workshop, 2020.
[15] Fan Lai, Yinwei Dai, Sanjay Sri Vallabh Singapuram, Jiachen Liu, Xiangfeng Zhu, Harsha V. Madhyastha, and Mosharaf Chowdhury. FedScale: Benchmarking Model and System Performance of Federated Learning at Scale. In ICML, volume 162, pages 11814–11827, 2022.
[16] TensorFlow Datasets, a collection of ready-to-use datasets. https://www.tensorflow.org/datasets.
[17] Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Julien Chaumont, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario Šaško, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clément Delangue, Théo Matussière, Lysandre Debut, Stas Bekman, Pierrick Cistac, Thibault Goehringer, Victor Mustar, François Lagunas, Alexander Rush, and Thomas Wolf. Datasets: A community library for natural language processing. In EMNLP, pages 175–184, 2021.
[18] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. J. Mach. Learn. Res., 21:140:1-140:67, 2020.
[19] Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascon, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrede Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao. Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn., 14(1-2):1-210, 2021.
[20] Daniel J Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Javier Fernandez-Marques, Yan Gao, Lorenzo Sani, Hei Li Kwing, Titouan Parcollet, Pedro PB de Gusmão, and Nicholas D Lane. Flower: A Friendly Federated Learning Research Framework. arXiv Preprint, 2020.
[21] Yuan Liang, Yange Guo, Yanxia Gong, Chunjie Luo, Jianfeng Zhan, and Yunyou Huang. FLBench: A Benchmark Suite for Federated Learning. In Intelligent Computing and Block Chain, pages 166-176. Springer, 2021.
[22] Sixu Hu, Yuan Li, Xu Liu, Qinbin Li, Zhaomin Wu, and Bingsheng He. The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems. ACM Trans. Intell. Syst. Technol., 13(4), 2022. ISSN 2157-6904. doi: 10.1145/3510540.
[23] Jae Hun Ro, Ananda Theertha Suresh, and Ke Wu. FedJAX: Federated learning simulation with JAX. arXiv Preprint, 2021.
[24] Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, and Viveck Cadambe. Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization. In NeurIPS, pages 11080-11092, 2019.
[25] Aymeric Dieuleveut and Kumar Kshitij Patel. Communication Trade-offs for Local-SGD with Large Step Size. In NeurIPS, pages 13579-13590, 2019.