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 …

The field of deep learning has witnessed significant progress, particularly in computer vision
(CV), natural language processing (NLP), and speech. The use of large-scale models …

Distributed training of foundation models, especially large language models (LLMs), is
communication-intensive and so has heavily relied on centralized data centers with fast …

The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual
benefits, presents a unique opportunity to unlock new possibilities in AI research, and …

Bilevel optimization has been successfully applied to many important machine learning
problems. Algorithms for solving bilevel optimization have been studied under various …

Large language models (LLMs) are useful in many NLP tasks and become more capable
with size, with the best open-source models having over 50 billion parameters. However …

Decentralized stochastic gradient descent (D-SGD) allows collaborative learning on
massive devices simultaneously without the control of a central server. However, existing …

Sustainability in computing is critical as environmental concerns rise. The cloud industry's
carbon footprint is significant and rapidly growing. We show that dynamic geospatial shifting …

The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …

While the numerous parameters in Large Language Models (LLMs) contribute to their
superior performance, this massive scale makes them inefficient and memory-hungry. Thus …