Smith, Virginia

Federated Optimization in Heterogeneous Networks

Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2018) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 4,023

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

Fair Resource Allocation in Federated Learning

Li, T., Sanjabi, M., Beirami, A., & Smith, V. (2019) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 475

Learning Context-Aware Policies from Multiple Smart Homes via Federated Multi-Task Learning

Yu, T., Li, T., Sun, Y., Nanda, S., Smith, V., Sekar, V., & Seshan, S. (2020) · 2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI)

Privacy-Preserving Technologies in DataAccess Control and Trust

Cited by 85

Validating Large Language Models with ReLM

Kuchnik, M., Smith, V., & Amvrosiadis, G. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 12