Ramage, Daniel

Cytoscape: A software Environment for integrated models of biomolecular interaction networks

Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research

Bioinformatics and Genomic NetworksGene Regulatory Network Analysis

Cited by 51,888

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

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

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

Federated Learning for Mobile Keyboard Prediction

Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., & Ramage, D. (2018) · arXiv

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

Cited by 1,148

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

Applied Federated Learning: Improving Google Keyboard Query Suggestions

Yang, T., Andrew, G., Eichner, H., Sun, H., Li, W., Kong, N., Ramage, D., & Beaufays, F. (2018) · arXiv

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

Cited by 684

Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning

Shejwalkar, V., Houmansadr, A., Kairouz, P., & Ramage, D. (2022) · 2022 IEEE Symposium on Security and Privacy (SP)

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 292

Federated Evaluation of On-device Personalization

Wang, K., Mathews, R., Kiddon, C., Eichner, H., Beaufays, F., & Ramage, D. (2019) · arXiv

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

Cited by 184

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

AirGapAgent: Protecting Privacy-Conscious Conversational Agents

Bagdasarian, E., Yi, R., Ghalebikesabi, S., Kairouz, P., Gruteser, M., Oh, S., Balle, B., & Ramage, D. (2024) · Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 28