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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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dc.contributor.authorKairouz, Peter-
dc.contributor.authorMcMahan, H Brendan-
dc.contributor.authorAvent, Brendan-
dc.contributor.authorBellet, Aurélien-
dc.contributor.authorBennis, Mehdi-
dc.contributor.authorNitin Bhagoji, Arjun-
dc.contributor.authorBonawitz, Kallista-
dc.contributor.authorCharles, Zachary-
dc.contributor.authorCormode, Graham-
dc.contributor.authorCummings, Rachel-
dc.contributor.authorD’Oliveira, Rafael GL-
dc.contributor.authorEichner, Hubert-
dc.contributor.authorEl Rouayheb, Salim-
dc.contributor.authorEvans, David-
dc.contributor.authorGardner, Josh-
dc.contributor.authorGarrett, Zachary-
dc.contributor.authorGascón, Adrià-
dc.contributor.authorGhazi, Badih-
dc.contributor.authorGibbons, Phillip B-
dc.contributor.authorGruteser, Marco-
dc.contributor.authorHarchaoui, Zaid-
dc.contributor.authorHe, Chaoyang-
dc.contributor.authorHe, Lie-
dc.contributor.authorHuo, Zhouyuan-
dc.contributor.authorHutchinson, Ben-
dc.contributor.authorHsu, Justin-
dc.contributor.authorJaggi, Martin-
dc.contributor.authorJavidi, Tara-
dc.contributor.authorJoshi, Gauri-
dc.contributor.authorKhodak, Mikhail-
dc.contributor.authorKonecný, Jakub-
dc.contributor.authorKorolova, Aleksandra-
dc.contributor.authorKoushanfar, Farinaz-
dc.contributor.authorKoyejo, Sanmi-
dc.contributor.authorLepoint, Tancrède-
dc.contributor.authorLiu, Yang-
dc.contributor.authorMittal, Prateek-
dc.contributor.authorMohri, Mehryar-
dc.contributor.authorNock, Richard-
dc.contributor.authorÖzgür, Ayfer-
dc.contributor.authorPagh, Rasmus-
dc.contributor.authorQi, Hang-
dc.contributor.authorRamage, Daniel-
dc.contributor.authorRaskar, Ramesh-
dc.contributor.authorRaykova, Mariana-
dc.contributor.authorSong, Dawn-
dc.contributor.authorSong, Weikang-
dc.contributor.authorStich, Sebastian U-
dc.contributor.authorSun, Ziteng-
dc.contributor.authorSuresh, Ananda Theertha-
dc.contributor.authorTramèr, Florian-
dc.contributor.authorVepakomma, Praneeth-
dc.contributor.authorWang, Jianyu-
dc.contributor.authorXiong, Li-
dc.contributor.authorXu, Zheng-
dc.contributor.authorYang, Qiang-
dc.contributor.authorYu, Felix X-
dc.contributor.authorYu, Han-
dc.contributor.authorZhao, Sen-
dc.identifier.citationKairouz, 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:10.1561/2200000083en_US
dc.description.abstractFederated 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.en_US
dc.format.extent1 - 210en_US
dc.relation.ispartofFoundations and Trends in Machine Learningen_US
dc.rightsAuthor's manuscripten_US
dc.titleAdvances and Open Problems in Federated Learningen_US
dc.typeJournal Articleen_US

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