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Advances and Open Problems in Federated Learning

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 G. L. ; 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

Foundations and trends in machine learning, 2021-01, Vol.14 (1–2), p.1-210 [Periódico revisado por pares]

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Título:
Advances and Open Problems in Federated Learning
Autor: 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 G. L. ; 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
Assuntos: Computer Science ; Cryptography and Security ; Machine Learning ; Statistics
É parte de: Foundations and trends in machine learning, 2021-01, Vol.14 (1–2), p.1-210
Descrição: 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 monograph discusses recent advances and presents an extensive collection of open problems and challenges.
Editor: Now Publishers
Idioma: Inglês

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Advances and Open Problems in Federated Learning

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