Mobile edge intelligence for large language models: A contemporary survey

G Qu, Q Chen, W Wei, Z Lin, X Chen… - arXiv preprint arXiv …, 2024 - arxiv.org
On-device large language models (LLMs), referring to running LLMs on edge devices, have
raised considerable interest owing to their superior privacy, reduced latency, and bandwidth …

Joint device scheduling and resource allocation for iscc-based multi-view-multi-task inference

D Wang, D Wen, Y He, Q Chen… - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
This article investigates an integrated sensing-communication-computation (ISCC)-based
multiview-multitask (MVMT) edge artificial intelligence inference system. Each device …

Resource Management for Low-latency Cooperative Fine-tuning of Foundation Models at the Network Edge

H Wu, X Chen, K Huang - arXiv preprint arXiv:2407.09873, 2024 - arxiv.org
The emergence of large-scale foundation models (FoMo's) that can perform human-like
intelligence motivates their deployment at the network edge for devices to access state-of …

Space-ground Fluid AI for 6G Edge Intelligence

Q Chen, Z Wang, X Chen, J Wen, D Zhou, S Ji… - arXiv preprint arXiv …, 2024 - arxiv.org
Edge artificial intelligence (AI) and space-ground integrated networks (SGIN) are two main
usage scenarios of the sixth-generation (6G) mobile networks. Edge AI supports pervasive …

Multi-Device Cooperative Fine-Tuning of Foundation Models at the Network Edge

H Wu, X Chen, K Huang - 2024 IEEE/CIC International …, 2024 - ieeexplore.ieee.org
The emergence of large-scale foundation models (FoMo's) that can perform human-like
intelligence inspires edge devices to access state-of-the-art artificial intelligence (AI) …