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H. Brendan McMahan
H. Brendan McMahan
Research Scientist, Google Seattle
Dirección de correo verificada de google.com - Página principal
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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
207602017
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
70592016
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
65942021
Federated Learning: Strategies for Improving Communication Efficiency
J Konečný
arXiv preprint arXiv:1610.05492, 2016
57662016
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
34352017
Towards federated learning at scale: Syste m design
K Bonawitz
arXiv preprint arXiv:1902.01046, 2019
33262019
Federated optimization: Distributed machine learning for on-device intelligence
J Konečný, HB McMahan, D Ramage, P Richtárik
arXiv preprint arXiv:1610.02527, 2016
23762016
Learning differentially private recurrent language models
HB McMahan, D Ramage, K Talwar, L Zhang
arXiv preprint arXiv:1710.06963, 2017
16152017
Adaptive federated optimization
S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečný, S Kumar, ...
arXiv preprint arXiv:2003.00295, 2020
15432020
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
15232018
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
11512013
Online convex optimization in the bandit setting: gradient descent without a gradient
AD Flaxman, AT Kalai, HB McMahan
arXiv preprint cs/0408007, 2004
10262004
Federated optimization: Distributed optimization beyond the datacenter
J Konečný, B McMahan, D Ramage
arXiv preprint arXiv:1511.03575, 2015
8202015
Federated learning: Collaborative machine learning without centralized training data
B McMahan, D Ramage
Google Research Blog 3, 2017
8062017
Can you really backdoor federated learning?
Z Sun, P Kairouz, AT Suresh, HB McMahan
arXiv preprint arXiv:1911.07963, 2019
6602019
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
5792016
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
5452018
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
5102018
Adaptive bound optimization for online convex optimization
HB McMahan, M Streeter
Proceedings of the 23rd Annual Conference on Learning Theory (COLT), 2010
4302010
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
4052003
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20