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Found 7 papers, 4 papers with code
Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice
We articulate fundamental mismatches between technical methods for machine unlearning in Generative AI, and documented aspirations for broader impact that these methods could have for law and policy.
Fine-Tuning Large Language Models with User-Level Differential Privacy
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 contributed by each user.
DrJAX: Scalable and Differentiable MapReduce Primitives in JAX
We present DrJAX, a JAX-based library designed to support large-scale distributed and parallel machine learning algorithms that use MapReduce-style operations.
Leveraging Function Space Aggregation for Federated Learning at Scale
Many federated learning algorithms, including the canonical Federated Averaging (FedAvg), take a direct (possibly weighted) average of the client parameter updates, motivated by results in distributed optimization.
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning
Finally, it is framework-agnostic.
JaxPruner: A concise library for sparsity research
This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research.
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory
A significant bottleneck in federated learning (FL) is the network communication cost of sending model updates from client devices to the central server.
