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Advances and Open Problems in Federated Learning
v1v2v3 (latest)

Advances and Open Problems in Federated Learning

10 December 2019
Peter Kairouz
H. B. McMahan
Brendan Avent
A. Bellet
M. Bennis
A. Bhagoji
Keith Bonawitz
Zachary B. Charles
Graham Cormode
Rachel Cummings
Rafael G. L. DÓliveira
Hubert Eichner
S. E. Rouayheb
David Evans
Josh Gardner
Zachary Garrett
Adria Gascon
Badih Ghazi
Phillip B. Gibbons
Marco Gruteser
Zaïd Harchaoui
Chaoyang He
Lie He
Zhouyuan Huo
Ben Hutchinson
Justin Hsu
Martin Jaggi
T. Javidi
Gauri Joshi
M. Khodak
Jakub Konecný
Aleksandra Korolova
F. Koushanfar
Oluwasanmi Koyejo
Tancrède Lepoint
Yang Liu
Prateek Mittal
M. Mohri
Richard Nock
A. Özgür
Rasmus Pagh
Mariana Raykova
Hang Qi
Daniel Ramage
Ramesh Raskar
Basel Alomair
Weikang Song
Sebastian U. Stich
Ziteng Sun
A. Suresh
Florian Tramèr
Praneeth Vepakomma
Jianyu Wang
Li Xiong
Zheng Xu
Qiang Yang
Felix X. Yu
Han Yu
Sen Zhao
    FedMLAI4CE
ArXiv (abs)PDFHTML

Papers citing "Advances and Open Problems in Federated Learning"

10 / 2,960 papers shown
Private Aggregation from Fewer Anonymous Messages
Private Aggregation from Fewer Anonymous MessagesInternational Conference on the Theory and Application of Cryptographic Techniques (EUROCRYPT), 2019
Badih Ghazi
Pasin Manurangsi
Rasmus Pagh
A. Velingker
FedML
211
57
0
24 Sep 2019
Manipulation Attacks in Local Differential Privacy
Manipulation Attacks in Local Differential PrivacyIEEE Symposium on Security and Privacy (IEEE S&P), 2019
Albert Cheu
Adam D. Smith
Jonathan R. Ullman
155
114
0
20 Sep 2019
From Server-Based to Client-Based Machine Learning: A Comprehensive
  Survey
From Server-Based to Client-Based Machine Learning: A Comprehensive SurveyACM Computing Surveys (ACM CSUR), 2019
Renjie Gu
Chaoyue Niu
Fan Wu
Guihai Chen
Chun Hu
Chengfei Lyu
Zhihua Wu
243
29
0
18 Sep 2019
Boosting Privately: Privacy-Preserving Federated Extreme Boosting for
  Mobile Crowdsensing
Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing
Yang Liu
Zhuo Ma
Ximeng Liu
Siqi Ma
Surya Nepal
R. Deng
FedML
145
64
0
24 Jul 2019
A Survey on Federated Learning Systems: Vision, Hype and Reality for
  Data Privacy and Protection
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and ProtectionIEEE Transactions on Knowledge and Data Engineering (TKDE), 2019
Yue Liu
Zeyi Wen
Zhaomin Wu
Sixu Hu
Naibo Wang
Yuan N. Li
Xu Liu
Bingsheng He
FedML
496
1,255
0
23 Jul 2019
Decentralized Deep Learning with Arbitrary Communication Compression
Decentralized Deep Learning with Arbitrary Communication CompressionInternational Conference on Learning Representations (ICLR), 2019
Anastasia Koloskova
Tao Lin
Sebastian U. Stich
Martin Jaggi
FedML
307
253
0
22 Jul 2019
SoK: Differential Privacies
SoK: Differential PrivaciesProceedings on Privacy Enhancing Technologies (PoPETs), 2019
Damien Desfontaines
Balázs Pejó
608
140
0
04 Jun 2019
SecureBoost: A Lossless Federated Learning Framework
SecureBoost: A Lossless Federated Learning Framework
Kewei Cheng
Tao Fan
Yilun Jin
Yang Liu
Tianjian Chen
Dimitrios Papadopoulos
Qiang Yang
FedML
297
659
0
25 Jan 2019
Fully Decentralized Joint Learning of Personalized Models and
  Collaboration Graphs
Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs
Valentina Zantedeschi
A. Bellet
Marc Tommasi
FedML
477
83
0
24 Jan 2019
Don't Use Large Mini-Batches, Use Local SGD
Don't Use Large Mini-Batches, Use Local SGD
Tao Lin
Sebastian U. Stich
Kumar Kshitij Patel
Martin Jaggi
730
454
0
22 Aug 2018
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