Charles, Zachary

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

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

DETOX: A redundancy-based framework for faster and more robust gradient aggregation

Rajput, S., Wang, H., Charles, Z., & Papailiopoulos, D. (2019) · arXiv

Stochastic Gradient Optimization TechniquesDistributed systems and fault tolerance

Cited by 72

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

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

Gradient Coding Using the Stochastic Block Model

Charles, Z., & Papailiopoulos, D. (2018) · 2018 IEEE International Symposium on Information Theory (ISIT)

Stochastic Gradient Optimization TechniquesDistributed systems and fault tolerance

Cited by 23