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cs.LG2024
Federated Automatic Differentiation
Keith Rush, Zachary Charles, Zachary Garrett
Federated learning (FL) is a general framework for learning across an axis of group partitioned data (heterogeneous clients) while preserving data privacy, under the orchestration…
cs.DC2024
DrJAX: Scalable and Differentiable MapReduce Primitives in JAX
Keith Rush, Zachary Charles, Zachary Garrett +2
We present DrJAX, a JAX-based library designed to support large-scale distributed and parallel machine learning algorithms that use MapReduce-style operations. DrJAX leverages JAX'…
cs.LG2024
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…