Konečný, Jakub

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

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

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

Improving Federated Learning Personalization via Model Agnostic Meta Learning

Jiang, Y., Konečný, J., Rush, K., & Kannan, S. (2019) · arXiv

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

Cited by 381

Randomized distributed mean estimation: Accuracy vs. communication

Konečný, J., & Richtárik, P. (2018) · Frontiers in Applied Mathematics and Statistics

Stochastic Gradient Optimization TechniquesDistributed Control Multi-Agent Systems

Cited by 69

On the Outsized Importance of Learning Rates in Local Update Methods

Charles, Z., & Konečný, J. (2020) · arXiv

Stochastic Gradient Optimization TechniquesPrivacy-Preserving Technologies in Data

Cited by 32