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Advances and Open Problems in Federated Learning

Author(s): Kairouz, Peter; McMahan, H Brendan; Avent, Brendan; Bellet, Aurélien; Bennis, Mehdi; et al

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Abstract: Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.
Publication Date: 2021
Citation: Kairouz, Peter, McMahan, H Brendan, Avent, Brendan, Bellet, Aurélien, Bennis, Mehdi, Nitin Bhagoji, Arjun, Bonawitz, Kallista, Charles, Zachary, Cormode, Graham, Cummings, Rachel, D’Oliveira, Rafael GL, Eichner, Hubert, El Rouayheb, Salim, Evans, David, Gardner, Josh, Garrett, Zachary, Gascón, Adrià, Ghazi, Badih, Gibbons, Phillip B, Gruteser, Marco, Harchaoui, Zaid, He, Chaoyang, He, Lie, Huo, Zhouyuan, Hutchinson, Ben, Hsu, Justin, Jaggi, Martin, Javidi, Tara, Joshi, Gauri, Khodak, Mikhail, Konecný, Jakub, Korolova, Aleksandra, Koushanfar, Farinaz, Koyejo, Sanmi, Lepoint, Tancrède, Liu, Yang, Mittal, Prateek, Mohri, Mehryar, Nock, Richard, Özgür, Ayfer, Pagh, Rasmus, Qi, Hang, Ramage, Daniel, Raskar, Ramesh, Raykova, Mariana, Song, Dawn, Song, Weikang, Stich, Sebastian U, Sun, Ziteng, Suresh, Ananda Theertha, Tramèr, Florian, Vepakomma, Praneeth, Wang, Jianyu, Xiong, Li, Xu, Zheng, Yang, Qiang, Yu, Felix X, Yu, Han, Zhao, Sen. (2021). Advances and Open Problems in Federated Learning. Foundations and Trends® in Machine Learning, 14 (1–2), 1 - 210. doi:10.1561/2200000083
DOI: doi:10.1561/2200000083
ISSN: 1935-8237
EISSN: 1935-8245
Pages: 1 - 210
Language: en
Type of Material: Journal Article
Journal/Proceeding Title: Foundations and Trends in Machine Learning
Version: Author's manuscript

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