H. Brendan McMahan
H. Brendan McMahan
Research Scientist, Google Seattle
Verified email at google.com - Homepage
Cited by
Cited by
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
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
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
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
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
Online convex optimization in the bandit setting: gradient descent without a gradient
AD Flaxman, AT Kalai, HB McMahan
arXiv preprint cs/0408007, 2004
Towards federated learning at scale: System design
K Bonawitz, H Eichner, W Grieskamp, D Huba, A Ingerman, V Ivanov, ...
arXiv preprint arXiv:1902.01046, 2019
Federated optimization: Distributed machine learning for on-device intelligence
J Konečný, HB McMahan, D Ramage, P Richtárik
arXiv preprint arXiv:1610.02527, 2016
Robust submodular observation selection
A Krause, HB McMahan, C Guestrin, A Gupta
Journal of Machine Learning Research 9 (Dec), 2761-2801, 2008
Learning differentially private recurrent language models
HB McMahan, D Ramage, K Talwar, L Zhang
arXiv preprint arXiv:1710.06963, 2017
Advances and open problems in federated learning
P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ...
arXiv preprint arXiv:1912.04977, 2019
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
Adaptive bound optimization for online convex optimization
HB McMahan, M Streeter
Proceedings of the 23rd Annual Conference on Learning Theory (COLT), 2010
Federated learning: Collaborative machine learning without centralized training data
B McMahan, D Ramage
Google Research Blog 3, 2017
Federated optimization: Distributed optimization beyond the datacenter
J Konečný, B McMahan, D Ramage
arXiv preprint arXiv:1511.03575, 2015
Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees
HB McMahan, M Likhachev, GJ Gordon
Proceedings of the 22nd international conference on Machine learning, 569-576, 2005
Follow-the-regularized-leader and mirror descent: Equivalence theorems and l1 regularization
HB McMahan
Proceedings of the 14th International Conference on Artificial Intelligence …, 2011
Online geometric optimization in the bandit setting against an adaptive adversary
HB McMahan, A Blum
International Conference on Computational Learning Theory, 109-123, 2004
cpsgd: Communication-efficient and differentially-private distributed sgd
N Agarwal, AT Suresh, FXX Yu, S Kumar, B McMahan
Advances in Neural Information Processing Systems, 7564-7575, 2018
Distributed mean estimation with limited communication
AT Suresh, XY Felix, S Kumar, HB McMahan
International Conference on Machine Learning, 3329-3337, 2017
The system can't perform the operation now. Try again later.
Articles 1–20