4 citations · 7 across the 3 of their papers we have counts for
3 papers
Fine-Tuning Large Language Models with User-Level Differential Privacy
Zachary Charles, Arun Ganesh, Ryan McKenna +4
We investigate practical and scalable algorithms for training large language models (LLMs) with user-level differential privacy (DP) in order to provably safeguard all the examples…
Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning
Zachary Charles, Kallista Bonawitz, Stanislav Chiknavaryan +2
Federated learning (FL) is a framework for machine learning across heterogeneous client devices in a privacy-preserving fashion. To date, most FL algorithms learn a "global" server…
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory
Nicole Mitchell, Johannes Ballé, Zachary Charles +1
A significant bottleneck in federated learning (FL) is the network communication cost of sending model updates from client devices to the central server. We present a comprehensive…