McMahan, H. Brendan

Communication-Efficient Learning of Deep Networks from Decentralized Data

McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 14,215

Advances and Open Problems in Federated Learning

Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al. (2019) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 7,857

Advances and Open Problems in Federated Learning

Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 7,772

Deep Learning with Differential Privacy

Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 6,578

Practical Secure Aggregation for Privacy-Preserving Machine Learning

Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

Cryptography and Data SecurityPrivacy-Preserving Technologies in Data

Cited by 3,956

Federated Learning: Strategies for Improving Communication Efficiency

Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 3,766

Towards Federated Learning at Scale: System Design

Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečný, J., Mazzocchi, S., McMahan, H. B., et al. (2019) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 2,334

Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Konečný, J., McMahan, H. B., Ramage, D., & Richtárik, P. (2016) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 1,563

LEAF: A Benchmark for Federated Settings

Caldas, S., Duddu, S. M. K., Wu, P., Li, T., Konečný, J., McMahan, H. B., Smith, V., & Talwalkar, A. (2018) · arXiv

Privacy-Preserving Technologies in DataDomain Adaptation and Few-Shot Learning

Cited by 894

Learning Differentially Private Recurrent Language Models

McMahan, H. B., Ramage, D., Talwar, K., & Zhang, L. (2017) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 849

Adaptive Federated Optimization

Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., & McMahan, H. B. (2020) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 840

Expanding the Reach of Federated Learning by Reducing Client Resource Requirements

Caldas, S., Konečny, J., McMahan, H. B., & Talwalkar, A. (2018) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 312

Adaptive bound optimization for online convex optimization

McMahan, H. B., & Streeter, M. (2010) · arXiv

Advanced Bandit Algorithms ResearchOptimization and Search Problems

Cited by 227

How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy

Ponomareva, N., Hazimeh, H., Kurakin, A., Xu, Z., Denison, C., McMahan, H. B., Vassilvitskii, S., Chien, S., & Thakurta, A. G. (2023) · Journal of Artificial Intelligence Research

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 166

Is Local SGD Better than Minibatch SGD?

Woodworth, B., Patel, K. K., Stich, S. U., Dai, Z., Bullins, B., McMahan, H. B., Shamir, O., & Srebro, N. (2020) · arXiv

Stochastic Gradient Optimization TechniquesDistributed Control Multi-Agent Systems

Cited by 131

Bounded real-time dynamic programming: RTDP with monotone upper bounds and performance guarantees

McMahan, H. B., Likhachev, M., & Gordon, G. J. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 128

A General Approach to Adding Differential Privacy to Iterative Training Procedures

McMahan, H. B., Andrew, G., Erlingsson, U., Chien, S., Mironov, I., Papernot, N., & Kairouz, P. (2018) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 126

Generative Models for Effective ML on Private, Decentralized Datasets

Augenstein, S., McMahan, H. B., Ramage, D., Ramaswamy, S., Kairouz, P., Chen, M., Mathews, R., & Arcas, B. A. Y. (2019) · arXiv

Privacy-Preserving Technologies in DataGenerative Adversarial Networks and Image Synthesis

Cited by 103

Semi-Cyclic Stochastic Gradient Descent

Eichner, H., Koren, T., McMahan, H. B., Srebro, N., & Talwar, K. (2019) · arXiv

Stochastic Gradient Optimization TechniquesPrivacy-Preserving Technologies in Data

Cited by 66

Training Production Language Models without Memorizing User Data

Ramaswamy, S., Thakkar, O., Mathews, R., Andrew, G., McMahan, H. B., & Beaufays, F. (2020) · arXiv

Privacy-Preserving Technologies in DataPrivacy, Security, and Data Protection

Cited by 37