Advances and Open Problems in Federated Learning
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Advances and Open Problems in Federated Learning
Auteur(s) :
Kairouz, Peter [Auteur]
Google Research
Mcmahan, Brendan [Auteur]
Google Research
Avent, Brendan [Auteur]
University of Southern California [USC]
Bellet, Aurelien [Auteur]
Machine Learning in Information Networks [MAGNET]
Bennis, Mehdi [Auteur]
Centre for Wireless Communications [University of Oulu] [CWC]
Bhagoji, Arjun Nitin [Auteur]
Princeton University
Bonawitz, Kallista [Auteur]
Google Research
Charles, Zachary [Auteur]
Google Research
Cormode, Graham [Auteur]
University of Warwick [Coventry]
Cummings, Rachel [Auteur]
Georgia Institute of Technology [Atlanta]
d'Oliveira, Rafael [Auteur]
Rouayheb, Salim El [Auteur]
Evans, David [Auteur]
University of Virginia
Gardner, Josh [Auteur]
University of Washington [Seattle]
Garrett, Zachary [Auteur]
Google Research
Gascón, Adrià [Auteur]
Google Research
Ghazi, Badih [Auteur]
Google Research
Gibbons, Phillip [Auteur]
Carnegie Mellon University [Pittsburgh] [CMU]
Gruteser, Marco [Auteur]
Google Research
Harchaoui, Zaid [Auteur]
University of Washington [Seattle]
He, Chaoyang [Auteur]
University of Southern California [USC]
He, Lie [Auteur]
Ecole Polytechnique Fédérale de Lausanne [EPFL]
Huo, Zhouyuan [Auteur]
University of Pittsburgh [PITT]
Hutchinson, Ben [Auteur]
Google Research
Hsu, Justin [Auteur]
University of Wisconsin-Madison
Jaggi, Martin [Auteur]
Ecole Polytechnique Fédérale de Lausanne [EPFL]
Javidi, Tara [Auteur]
University of California [San Diego] [UC San Diego]
Joshi, Gauri [Auteur]
Carnegie Mellon University [Pittsburgh] [CMU]
Khodak, Mikhail [Auteur]
Carnegie Mellon University [Pittsburgh] [CMU]
Konečný, Jakub [Auteur]
Google Research
Korolova, Aleksandra [Auteur]
University of Southern California [USC]
Koushanfar, Farinaz [Auteur]
University of California [San Diego] [UC San Diego]
Koyejo, Sanmi [Auteur]
Google Research
University of Illinois at Urbana-Champaign [Urbana] [UIUC]
Lepoint, Tancrède [Auteur]
Google Research
Liu, Yang [Auteur]
Nanyang Technological University [Singapour]
Mittal, Prateek [Auteur]
Princeton University
Mohri, Mehryar [Auteur]
Google Research
Nock, Richard [Auteur]
Australian National University [ANU]
Ozgür, Ayfer [Auteur]
Pagh, Rasmus [Auteur]
Google Research
IT University of Copenhagen [ITU]
Raykova, Mariana [Auteur]
Google Research
Qi, Hang [Auteur]
Google Research
Ramage, Daniel [Auteur]
Google Research
Raskar, Ramesh [Auteur]
Massachusetts Institute of Technology [MIT]
Song, Dawn [Auteur]
University of California [Berkeley] [UC Berkeley]
Song, Weikang [Auteur]
Google Research
Stich, Sebastian [Auteur]
Ecole Polytechnique Fédérale de Lausanne [EPFL]
Sun, Ziteng [Auteur]
Cornell University [New York]
Suresh, Ananda Theertha [Auteur]
Google Research
Tramèr, Florian [Auteur]
Vepakomma, Praneeth [Auteur]
Massachusetts Institute of Technology [MIT]
Wang, Jianyu [Auteur]
Carnegie Mellon University [Pittsburgh] [CMU]
Xiong, Li [Auteur]
Emory University [Atlanta, GA]
Xu, Zheng [Auteur]
Google Research
Yang, Qiang [Auteur]
Hong Kong University of Science and Technology [HKUST]
Yu, Felix [Auteur]
Google Research
Yu, Han [Auteur]
Nanyang Technological University [Singapour]
Zhao, Sen [Auteur]
Google Research
Google Research
Mcmahan, Brendan [Auteur]
Google Research
Avent, Brendan [Auteur]
University of Southern California [USC]
Bellet, Aurelien [Auteur]
Machine Learning in Information Networks [MAGNET]
Bennis, Mehdi [Auteur]
Centre for Wireless Communications [University of Oulu] [CWC]
Bhagoji, Arjun Nitin [Auteur]
Princeton University
Bonawitz, Kallista [Auteur]
Google Research
Charles, Zachary [Auteur]
Google Research
Cormode, Graham [Auteur]
University of Warwick [Coventry]
Cummings, Rachel [Auteur]
Georgia Institute of Technology [Atlanta]
d'Oliveira, Rafael [Auteur]
Rouayheb, Salim El [Auteur]
Evans, David [Auteur]
University of Virginia
Gardner, Josh [Auteur]
University of Washington [Seattle]
Garrett, Zachary [Auteur]
Google Research
Gascón, Adrià [Auteur]
Google Research
Ghazi, Badih [Auteur]
Google Research
Gibbons, Phillip [Auteur]
Carnegie Mellon University [Pittsburgh] [CMU]
Gruteser, Marco [Auteur]
Google Research
Harchaoui, Zaid [Auteur]
University of Washington [Seattle]
He, Chaoyang [Auteur]
University of Southern California [USC]
He, Lie [Auteur]
Ecole Polytechnique Fédérale de Lausanne [EPFL]
Huo, Zhouyuan [Auteur]
University of Pittsburgh [PITT]
Hutchinson, Ben [Auteur]
Google Research
Hsu, Justin [Auteur]
University of Wisconsin-Madison
Jaggi, Martin [Auteur]
Ecole Polytechnique Fédérale de Lausanne [EPFL]
Javidi, Tara [Auteur]
University of California [San Diego] [UC San Diego]
Joshi, Gauri [Auteur]
Carnegie Mellon University [Pittsburgh] [CMU]
Khodak, Mikhail [Auteur]
Carnegie Mellon University [Pittsburgh] [CMU]
Konečný, Jakub [Auteur]
Google Research
Korolova, Aleksandra [Auteur]
University of Southern California [USC]
Koushanfar, Farinaz [Auteur]
University of California [San Diego] [UC San Diego]
Koyejo, Sanmi [Auteur]
Google Research
University of Illinois at Urbana-Champaign [Urbana] [UIUC]
Lepoint, Tancrède [Auteur]
Google Research
Liu, Yang [Auteur]
Nanyang Technological University [Singapour]
Mittal, Prateek [Auteur]
Princeton University
Mohri, Mehryar [Auteur]
Google Research
Nock, Richard [Auteur]
Australian National University [ANU]
Ozgür, Ayfer [Auteur]
Pagh, Rasmus [Auteur]
Google Research
IT University of Copenhagen [ITU]
Raykova, Mariana [Auteur]
Google Research
Qi, Hang [Auteur]
Google Research
Ramage, Daniel [Auteur]
Google Research
Raskar, Ramesh [Auteur]
Massachusetts Institute of Technology [MIT]
Song, Dawn [Auteur]
University of California [Berkeley] [UC Berkeley]
Song, Weikang [Auteur]
Google Research
Stich, Sebastian [Auteur]
Ecole Polytechnique Fédérale de Lausanne [EPFL]
Sun, Ziteng [Auteur]
Cornell University [New York]
Suresh, Ananda Theertha [Auteur]
Google Research
Tramèr, Florian [Auteur]
Vepakomma, Praneeth [Auteur]
Massachusetts Institute of Technology [MIT]
Wang, Jianyu [Auteur]
Carnegie Mellon University [Pittsburgh] [CMU]
Xiong, Li [Auteur]
Emory University [Atlanta, GA]
Xu, Zheng [Auteur]
Google Research
Yang, Qiang [Auteur]
Hong Kong University of Science and Technology [HKUST]
Yu, Felix [Auteur]
Google Research
Yu, Han [Auteur]
Nanyang Technological University [Singapour]
Zhao, Sen [Auteur]
Google Research
Titre de la revue :
Foundations and Trends in Machine Learning
Pagination :
1-210
Éditeur :
Now Publishers
Date de publication :
2021
ISSN :
1935-8237
Discipline(s) HAL :
Informatique [cs]/Apprentissage [cs.LG]
Statistiques [stat]/Machine Learning [stat.ML]
Informatique [cs]/Cryptographie et sécurité [cs.CR]
Statistiques [stat]/Machine Learning [stat.ML]
Informatique [cs]/Cryptographie et sécurité [cs.CR]
Résumé en anglais : [en]
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), ...
Lire la suite >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.Lire moins >
Lire la suite >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.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Projet ANR :
Collections :
Source :
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- http://arxiv.org/pdf/1912.04977
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- 1912.04977
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