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H. Brendan McMahan
H. Brendan McMahan
Research Scientist, Google Seattle
Geverifieerd e-mailadres voor google.com - Homepage
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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
192402017
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
66702016
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
61202021
Federated Learning: Strategies for Improving Communication Efficiency
J Konečnı
arXiv preprint arXiv:1610.05492, 2016
55282016
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
32452017
Towards federated learning at scale: Syste m design
K Bonawitz
arXiv preprint arXiv:1902.01046, 2019
31812019
Federated optimization: Distributed machine learning for on-device intelligence
J Konečnı, HB McMahan, D Ramage, P Richtárik
arXiv preprint arXiv:1610.02527, 2016
22672016
Learning differentially private recurrent language models
HB McMahan, D Ramage, K Talwar, L Zhang
arXiv preprint arXiv:1710.06963, 2017
15402017
Leaf: A benchmark for federated settings
S Caldas, SMK Duddu, P Wu, T Li, J Konečnı, HB McMahan, V Smith, ...
arXiv preprint arXiv:1812.01097, 2018
14312018
Adaptive federated optimization
S Reddi, Z Charles, M Zaheer, Z Garrett, K Rush, J Konečnı, S Kumar, ...
arXiv preprint arXiv:2003.00295, 2020
14162020
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
11392013
Online convex optimization in the bandit setting: gradient descent without a gradient
AD Flaxman, AT Kalai, HB McMahan
arXiv preprint cs/0408007, 2004
10072004
Federated optimization: Distributed optimization beyond the datacenter
J Konečnı, B McMahan, D Ramage
arXiv preprint arXiv:1511.03575, 2015
7982015
Federated learning: Collaborative machine learning without centralized training data
B McMahan, D Ramage
Google Research Blog 3, 2017
7822017
Can you really backdoor federated learning?
Z Sun, P Kairouz, AT Suresh, HB McMahan
arXiv preprint arXiv:1911.07963, 2019
6122019
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
5432016
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
5292018
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
4742018
Adaptive bound optimization for online convex optimization
HB McMahan, M Streeter
Proceedings of the 23rd Annual Conference on Learning Theory (COLT), 2010
4162010
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
3932003
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Artikelen 1–20