Recently, foundation models, particularly large language models (LLMs), have
demonstrated an impressive ability to adapt to various tasks by fine-tuning diverse …

Federated learning has enabled multiple parties to collaboratively train large language
models without directly sharing their data (FedLLM). Following this training paradigm, the …

Large language models are rapidly gaining popularity and have been widely adopted in real-
world applications. While the quality of training data is essential, privacy concerns arise …

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 …

Fine-tuning is a common and effective method for tailoring large language models (LLMs) to
specialized tasks and applications. In this paper, we study the privacy implications of fine …

Large Language Models (LLMs), such as LLaMA and GPT-4, have transformed the
paradigm of natural language comprehension and generation. Despite their impressive …

On-device training is currently the most common approach for training machine learning
(ML) models on private, distributed user data. Despite this, on-device training has several …

In many applications of federated learning (FL), clients desire models that are personalized
using their local data, yet are also robust in the sense that they retain general global …

The reliance of language model training on massive amounts of computation and vast
datasets scraped from potentially low-quality, copyrighted, or sensitive data has come into …

Federated learning (FL) is an emerging machine learning (ML) training paradigm where
clients own their data and collaborate to train a global model, without revealing any data to …