Advances and Open Problems in Federated Learning
Kairouz, Peter; McMahan, H. Brendan; Avent, Brendan; Bellet, Aurelien; Bennis, Mehdi; Bhagoji, Arjun Nitin; Bonawitz, Kallista; Charles, Zachary; Cormode, Graham; Cummings, Rachel; D'Oliveira, Rafael G. L.; Eichner, Hubert; El Rouayheb, Salim; Evans, David; Gardner, Josh; Garrett, Zachary; Gascon, Adria; 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; Konecny, Jakub; Korolova, Aleksandra; Koushanfar, Farinaz; Koyejo, Sanmi; Lepoint, Tancrede; Liu, Yang; Mittal, Prateek; Mohri, Mehryar; Nock, Richard; Ozgur, Ayfer; Pagh, Rasmus; Qi, Hang; Ramage, Daniel; Raskar, Ramesh; Raykova, Mariana; Song, Dawn; Song, Weikang; Stich, Sebastian U.; Sun, Ziteng; Suresh, Ananda Theertha; Tramer, Florian; Vepakomma, Praneeth; Wang, Jianyu; Xiong, Li; Xu, Zheng; Yang, Qiang; Yu, Felix X.; Yu, Han; Zhao, Sen
2021
Résumé
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.
Détails
Titre
Advances and Open Problems in Federated Learning
Auteur(s)
Kairouz, Peter ; McMahan, H. Brendan ; Avent, Brendan ; Bellet, Aurelien ; Bennis, Mehdi ; Bhagoji, Arjun Nitin ; Bonawitz, Kallista ; Charles, Zachary ; Cormode, Graham ; Cummings, Rachel ; D'Oliveira, Rafael G. L. ; Eichner, Hubert ; El Rouayheb, Salim ; Evans, David ; Gardner, Josh ; Garrett, Zachary ; Gascon, Adria ; 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 ; Konecny, Jakub ; Korolova, Aleksandra ; Koushanfar, Farinaz ; Koyejo, Sanmi ; Lepoint, Tancrede ; Liu, Yang ; Mittal, Prateek ; Mohri, Mehryar ; Nock, Richard ; Ozgur, Ayfer ; Pagh, Rasmus ; Qi, Hang ; Ramage, Daniel ; Raskar, Ramesh ; Raykova, Mariana ; Song, Dawn ; Song, Weikang ; Stich, Sebastian U. ; Sun, Ziteng ; Suresh, Ananda Theertha ; Tramer, Florian ; Vepakomma, Praneeth ; Wang, Jianyu ; Xiong, Li ; Xu, Zheng ; Yang, Qiang ; Yu, Felix X. ; Yu, Han ; Zhao, Sen
Publié dans
Foundations And Trends In Machine Learning
Volume
14
Numéro
1-2
Pages
1-210
Date
2021-01-01
Editeur
Hanover, NOW PUBLISHERS INC
ISSN
1935-8237
1935-8245
1935-8245
Mots-clés (libres)
Autres identifiant(s)
Afficher la publication dans Web of Science
Laboratoires
MLO
Le document apparaît dans
Production scientifique et compétences > I&C - Faculté Informatique & Communications > IINFCOM > MLO - Laboratoire d'apprentissage automatique et d'optimisation
Publications validées par des pairs
Travail produit à l'EPFL
Articles de journaux
Publié
Publications validées par des pairs
Travail produit à l'EPFL
Articles de journaux
Publié
Date de création de la notice
2021-07-17