Synchronous vs. Asynchronous Federated Learning Models
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Communication-Efficient Learning of Deep Networks from Decentralized Data, H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Aguera y Arcas, 2017Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Vol. 54 (PMLR (Proceedings of Machine Learning Research))DOI: 10.48550/arXiv.1710.06002 - Introduces the Federated Averaging (FedAvg) algorithm, a canonical synchronous federated learning approach, and analyzes its communication efficiency.
Advances and Open Problems in Federated Learning, Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, 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 Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède 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, Sen Zhao, 2021Foundations and Trends® in Machine Learning, Vol. 14 (Now Publishers)DOI: 10.1561/2200000083 - A comprehensive review of federated learning, discussing its foundations, applications, challenges, and future research directions, including an overview of synchronous and asynchronous approaches.