Követés
H. Brendan McMahan
H. Brendan McMahan
Research Scientist, Google Seattle
E-mail megerősítve itt: google.com - Kezdőlap
Cím
Hivatkozott rá
Hivatkozott rá
Év
Communication-efficient learning of deep networks from decentralized data
HB McMahan, E Moore, D Ramage, S Hampson, B Agüera y Arcas
Proceedings of the 20 th International Conference on Artificial Intelligence …, 2017
75152017
Deep learning with differential privacy
M Abadi, A Chu, I Goodfellow, HB McMahan, I Mironov, K Talwar, L Zhang
Proceedings of the 2016 ACM SIGSAC conference on computer and communications …, 2016
35712016
Federated learning: Strategies for improving communication efficiency
J Konečný, HB McMahan, FX Yu, P Richtárik, AT Suresh, D Bacon
arXiv preprint arXiv:1610.05492, 2016
30412016
Advances and open problems in federated learning
P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ...
Foundations and Trends® in Machine Learning 14 (1–2), 1-210, 2021
25442021
Practical secure aggregation for privacy-preserving machine learning
K Bonawitz, V Ivanov, B Kreuter, A Marcedone, HB McMahan, S Patel, ...
proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications …, 2017
16992017
Towards federated learning at scale: System design
K Bonawitz, H Eichner, W Grieskamp, D Huba, A Ingerman, V Ivanov, ...
Proceedings of Machine Learning and Systems 1, 374-388, 2019
16882019
Federated optimization: Distributed machine learning for on-device intelligence
J Konečný, HB McMahan, D Ramage, P Richtárik
arXiv preprint arXiv:1610.02527, 2016
12542016
Ad click prediction: a view from the trenches
HB McMahan, G Holt, D Sculley, M Young, D Ebner, J Grady, L Nie, ...
Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013
9512013
Learning differentially private recurrent language models
HB McMahan, D Ramage, K Talwar, L Zhang
arXiv preprint arXiv:1710.06963, 2017
834*2017
Online convex optimization in the bandit setting: gradient descent without a gradient
AD Flaxman, AT Kalai, HB McMahan
arXiv preprint cs/0408007, 2004
7362004
Leaf: A benchmark for federated settings
S Caldas, SMK Duddu, P Wu, T Li, J Konečný, HB McMahan, V Smith, ...
arXiv preprint arXiv:1812.01097, 2018
6602018
Adaptive federated optimization
S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečný, S Kumar, ...
arXiv preprint arXiv:2003.00295, 2020
4952020
Federated learning: Collaborative machine learning without centralized training data
B McMahan, D Ramage
Google Research Blog 3, 2017
4882017
Federated optimization: Distributed optimization beyond the datacenter
J Konečný, B McMahan, D Ramage
arXiv preprint arXiv:1511.03575, 2015
4862015
Robust Submodular Observation Selection.
A Krause, HB McMahan, C Guestrin, A Gupta
Journal of Machine Learning Research 9 (12), 2008
3212008
cpSGD: Communication-efficient and differentially-private distributed SGD
N Agarwal, AT Suresh, FXX Yu, S Kumar, B McMahan
Advances in Neural Information Processing Systems 31, 2018
3182018
Adaptive bound optimization for online convex optimization
HB McMahan, M Streeter
Proceedings of the 23rd Annual Conference on Learning Theory (COLT), 2010
3152010
Planning in the presence of cost functions controlled by an adversary
HB McMahan, GJ Gordon, A Blum
Proceedings of the 20th International Conference on Machine Learning (ICML …, 2003
3062003
Practical secure aggregation for federated learning on user-held data
K Bonawitz, V Ivanov, B Kreuter, A Marcedone, HB McMahan, S Patel, ...
arXiv preprint arXiv:1611.04482, 2016
2782016
Expanding the reach of federated learning by reducing client resource requirements
S Caldas, J Konečny, HB McMahan, A Talwalkar
arXiv preprint arXiv:1812.07210, 2018
2572018
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