Large language models (LLMs) have demonstrated great capabilities in various natural
language understanding and generation tasks. These pre-trained LLMs can be further …

The immense evolution in Large Language Models (LLMs) has underscored the importance
of massive, heterogeneous, and high-quality data. A data recipe is a mixture of data from …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

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 …

Large foundation models, including large language models (LLMs), vision transformers
(ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine …

The confluence of Federated Learning (FL) and Large Language Models (LLMs) is ushering
in a new era in privacy-preserving natural language processing. However, the intensive …

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

Large Language Models (LLMs) are transforming the landscape of mobile intelligence.
Federated Learning (FL), a method to preserve user data privacy, is often employed in fine …

Federated Learning (FL) has recently been applied to the parameter-efficient fine-tuning of
Large Language Models (LLMs). While promising, it raises significant challenges due to the …

At face value, this essay is about understanding a fairly esoteric governance tool called
compute thresholds. However, in order to grapple with whether these thresholds will achieve …