Zobrazeno 1 - 10
of 21
pro vyhledávání: '"H. Brendan McMahan"'
Autor:
Lie He, Sebastian U. Stich, Mariana Raykova, Phillip B. Gibbons, Mehryar Mohri, David Evans, Badih Ghazi, Felix X. Yu, Sen Zhao, Jianyu Wang, Zheng Xu, Weikang Song, Prateek Mittal, Ramesh Raskar, Zachary Garrett, Farinaz Koushanfar, H. Brendan McMahan, Ayfer Ozgur, Mikhail Khodak, Rafael G. L. D'Oliveira, Jakub Konecní, Aurélien Bellet, Arjun Nitin Bhagoji, Hubert Eichner, Han Yu, Adrià Gascón, Ananda Theertha Suresh, Sanmi Koyejo, Praneeth Vepakomma, Josh Gardner, Chaoyang He, Florian Tramèr, Tancrède Lepoint, Salim El Rouayheb, Peter Kairouz, Li Xiong, Kallista Bonawitz, Rasmus Pagh, Tara Javidi, Mehdi Bennis, Dawn Song, Martin Jaggi, Zhouyuan Huo, Hang Qi, Gauri Joshi, Qiang Yang, Richard Nock, Yang Liu, Brendan Avent, Justin Hsu, Rachel Cummings, Graham Cormode, Marco Gruteser, Aleksandra Korolova, Ziteng Sun, Zaid Harchaoui, Ben Hutchinson, Zachary Charles, Daniel Ramage
Publikováno v:
Foundations and Trends in Machine Learning
Foundations and Trends in Machine Learning, 2021, 14 (1-2), pp.1-210
Foundations and Trends in Machine Learning, Now Publishers, 2021, 14 (1-2), pp.1-210
Foundations and Trends in Machine Learning, 2021, 14 (1-2), pp.1-210
Foundations and Trends in Machine Learning, Now Publishers, 2021, 14 (1-2), pp.1-210
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 d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b1ccc10027ba1ce68ce0210510e8bdc
https://inria.hal.science/hal-02406503v2/document
https://inria.hal.science/hal-02406503v2/document
Autor:
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawit, 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, Sen Zhao
The term Federated Learning was coined as recently as 2016 to describe a machine learning setting where multiple entities collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client's
Autor:
Aaron Segal, Karn Seth, Sarvar Patel, Daniel Ramage, Ben Kreuter, Antonio Marcedone, Vladimir Ivanov, H. Brendan McMahan, Keith Bonawitz
Publikováno v:
CCS
We design a novel, communication-efficient, failure-robust protocol for secure aggregation of high-dimensional data. Our protocol allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner (i.e. without
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f2db1228fa9ad0fcdd1cbc436b0a2bdb
https://doi.org/10.1145/3133956.3133982
https://doi.org/10.1145/3133956.3133982
Autor:
Ian Goodfellow, Ilya Mironov, Martín Abadi, Úlfar Erlingsson, Kunal Talwar, Li Zhang, Nicolas Papernot, H. Brendan McMahan
Publikováno v:
CSF
The recent, remarkable growth of machine learning has led to intense interest in the privacy of the data on which machine learning relies, and to new techniques for preserving privacy. However, older ideas about privacy may well remain valid and usef
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b7f61b0ce691cb680c743d613219c7d
https://doi.org/10.1109/csf.2017.10
https://doi.org/10.1109/csf.2017.10
Autor:
Ian Goodfellow, H. Brendan McMahan, Kunal Talwar, Andy Chu, Ilya Mironov, Martín Abadi, Li Zhang
Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information. Th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e38db93c125fad895984ffdf624e707
Publikováno v:
Discrete Applied Mathematics. 137(2):213-222
We present two efficient algorithms constructing a spanning tree with minimum eccentricity of a source, for a given graph with weighted edges and a set of source vertices. The first algorithm is both simpler to implement and faster of the two. The se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b1cc49bf44c64db351cb4bf9d4a1ff58
Autor:
Julian Paul Grady, Arnar Mar Hrafnkelsson, Lan Nie, Martin Wattenberg, J. Kubica, Gary Holt, Todd Phillips, Dan Liu, Daniel Golovin, Sharat Chikkerur, Michael Young, Tom Boulos, Eugene Davydov, Dietmar Ebner, D. Sculley, H. Brendan McMahan
Publikováno v:
KDD
Predicting ad click-through rates (CTR) is a massive-scale learning problem that is central to the multi-billion dollar online advertising industry. We present a selection of case studies and topics drawn from recent experiments in the setting of a d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::12dd59169aa9d94eb135b4ed0acdad77
https://doi.org/10.1145/2487575.2488200
https://doi.org/10.1145/2487575.2488200
Publikováno v:
ICML
Given observed data and a collection of parameterized candidate models, a 1 -- α confidence region in parameter space provides useful insight as to those models which are a good fit to the data, all while keeping the probability of incorrect exclusi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::19d6731338592209b0a526031ab5f614
https://doi.org/10.1145/1273496.1273509
https://doi.org/10.1145/1273496.1273509
Publikováno v:
ICML
MDPs are an attractive formalization for planning, but realistic problems often have intractably large state spaces. When we only need a partial policy to get from a fixed start state to a goal, restricting computation to states relevant to this task
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::64c618d37459571b7ed243c3784ba6a8
https://doi.org/10.1145/1102351.1102423
https://doi.org/10.1145/1102351.1102423
"We study the problem of computing the optimal value function for a Markov decision process with positive costs. Computing this function quickly and accurately is a basic step in many schemes for deciding how to act in stochastic environments. There
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::570ad2d1537636300b2ae61174e6f8db