Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

On-device large language models (LLMs), referring to running LLMs on edge devices, have
raised considerable interest since they are more cost-effective, latency-efficient, and privacy …

The integration of Large Language Models (LLMs) and Edge Intelligence (EI) introduces a
groundbreaking paradigm for intelligent edge devices. With their capacity for human-like …

Small language models (SLMs), despite their widespread adoption in modern smart
devices, have received significantly less academic attention compared to their large …

Integrated satellite, aerial, and terrestrial networks (ISATNs) represent a sophisticated
convergence of diverse communication technologies to ensure seamless connectivity …

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …

The advent of large language models (LLMs) revolutionized natural language processing
applications, and running LLMs on edge devices has become increasingly attractive for …

Transformers have recently revolutionized the machine learning (ML) landscape, gradually
making their way into everyday tasks and equipping our computers with" sparks of …

On-device large language models (LLMs) are catalyzing novel mobile applications such as
UI task automation and personalized email auto-reply, without giving away users' private …

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 …