Advances and open problems in federated learning

Kairouz, Peter; McMahan, H. Brendan; Avent, Brendan; Bellet, Aurélien; 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; Gascón, Adriá; 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; Özgür, Ayfer; Pagh, Rasmus; Qi, Hang; Ramage, Daniel; Raskar, Ramesh; Raykova, Mariana; Song, Dawn; Song, Weikang; Stich, Sebastian U.; Sun, Ziteng; Theertha Suresh, Ananda; Tramér, Florian; Vepakomma, Praneeth; Wang, Jianyu; Xiong, Li; Xu, Zheng; Yang, Qiang; Yu, Felix X.; Yu, Han; Zhao, Sen

JultikaUniversity of Oulu repository

	Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu and Sen Zhao (2021), "Advances and Open Problems in Federated Learning", Foundations and Trends® in Machine Learning: Vol. 14: No. 1–2, pp 1-210. http://dx.doi.org/10.1561/2200000083 Advances and open problems in federated learning Saved in:
Author:	Kairouz, Peter¹; McMahan, H. Brendan¹; Avent, Brendan²; Bellet, Aurélien³; Bennis, Mehdi⁴; Bhagoji, Arjun Nitin⁵; Bonawitz, Kallista¹; Charles, Zachary¹; Cormode, Graham⁶; Cummings, Rachel⁷; D’Oliveira, Rafael G.L.⁸; Eichner, Hubert¹; El Rouayheb, Salim⁸; Evans, David⁹; Gardner, Josh¹⁰; Garrett, Zachary¹; Gascón, Adriá¹; Ghazi, Badih¹; Gibbons, Phillip B.¹¹; Gruteser, Marco^1,8; Harchaoui, Zaid¹⁰; He, Chaoyang²; He, Lie¹²; Huo, Zhouyuan¹³; Hutchinson, Ben¹; Hsu, Justin¹⁴; Jaggi, Martin¹²; Javidi, Tara¹⁵; Joshi, Gauri¹¹; Khodak, Mikhail¹¹; Konecny, Jakub¹; Korolova, Aleksandra²; Koushanfar, Farinaz¹⁵; Koyejo, Sanmi^1,16; Lepoint, Tancrede¹; Liu, Yang¹⁷; Mittal, Prateek⁵; Mohri, Mehryar¹; Nock, Richard¹⁸; Özgür, Ayfer¹⁹; Pagh, Rasmus^1,20; Qi, Hang¹; Ramage, Daniel¹; Raskar, Ramesh²¹; Raykova, Mariana¹; Song, Dawn²²; Song, Weikang¹; Stich, Sebastian U.¹²; Sun, Ziteng²³; Theertha Suresh, Ananda¹; Tramér, Florian¹⁹; Vepakomma, Praneeth²¹; Wang, Jianyu¹¹; Xiong, Li²⁴; Xu, Zheng¹; Yang, Qiang²⁵; Yu, Felix X.¹; Yu, Han¹⁷; Zhao, Sen¹
Organizations:	¹Google Research ²University of Southern California ³INRIA ⁴University of Oulu ⁵Princeton University ⁶University of Warwick ⁷Georgia Institute of Technology ⁸Rutgers University ⁹University of Virginia ¹⁰University of Washington ¹¹Carnegie Mellon University ¹²Ecole Polytechnique Fédérale de Lausanne ¹³University of Pittsburgh ¹⁴University of Wisconsin–Madison ¹⁵University of California San Diego ¹⁶University of Illinois Urbana-Champaign ¹⁷Nanyang Technological University ¹⁸Australian National University ¹⁹Stanford University ²⁰IT University of Copenhagen ²¹Massachusetts Institute of Technology ²²University of California Berkeley ²³Cornell University ²⁴Emory University ²⁵Hong Kong University of Science and Technology
Format:	article
Version:	accepted version
Access:	open
Online Access:	PDF Full Text (PDF, 0.9 MB)
Persistent link:	http://urn.fi/urn:nbn:fi-fe2023022828866
Language:	English
Published:	Now Publishers, 2021
Publish Date:	2023-02-28
Description:	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 monograph discusses recent advances and presents an extensive collection of open problems and challenges. see all 
Series:	Foundations and trends in machine learning
ISSN:	1935-8237
ISSN-E:	1935-8245
ISSN-L:	1935-8237
Volume:	14
Issue:	1-2
Pages:	1 - 210
DOI:	10.1561/2200000083
OADOI:	https://oadoi.org/10.1561/2200000083
Type of Publication:	A1 Journal article – refereed
Field of Science:	213 Electronic, automation and communications engineering, electronics
Subjects:	Access control Anonymity Artificial Intelligence methods in security and privacy Big data analytics and privacy Clustering Communication complexity Computational complexity Cryptology and data security Data compression Data mining Database theory Deep learning Distributed computing Distributed systems security and privacy Ethics Information retrieval Information theory and computer science Information theory and statistics Optimization Privacy Privacy-preserving systems Quantization Robustness Security Statistical learning theory Article
Copyright information:	© 2021 Peter Kairouz, H. Brendan McMahan, et al. The final publication is available from now publishers via http://dx.doi.org/10.1561/2200000083

Helpdesk

JultikaUniversity of Oulu repository

Similar Items

Advances and open problems in federated learning

Abstract