31 citations · 55 across the 10 of their papers we have counts for
11 papers
BALANCE: Hybrid Autoregressive-Speculative LLM Inference at the Network Edge
Guanqiao Qu, Shuo Chen, Qian Chen +2
Edge inference is a promising paradigm to provide large language model (LLM) inference services in next-generation mobile networks. LLM inference mainly relies on two approaches: A…
SLIDE: Simultaneous Model Downloading and Inference at the Wireless Network Edge
Guanqiao Qu, Tao Li, Qian Chen +2
To support on-device inference, the next-generation mobile networks are expected to support real-time model downloading services to mobile users. However, powerful AI models typica…
PartialLoading: User Scheduling and Bandwidth Allocation for Parameter-sharing Edge Inference
Guanqiao Qu, Qian Chen, Xianhao Chen +2
By provisioning inference offloading services, edge inference drives the rapid growth of AI applications at network edge. However, how to reduce the inference latency remains a sig…
Mobile Edge Intelligence for Large Language Models: A Contemporary Survey
Guanqiao Qu, Qiyuan Chen, Wei Wei +3
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 priv…
TrimCaching: Parameter-sharing AI Model Caching in Wireless Edge Networks
Guanqiao Qu, Zheng Lin, Fangming Liu +2
Next-generation mobile networks are expected to facilitate fast AI model downloading to end users. By caching models on edge servers, mobile networks can deliver models to end user…
TrimCaching: Parameter-sharing Edge Caching for AI Model Downloading
Guanqiao Qu, Zheng Lin, Qian Chen +4
Next-generation mobile networks are expected to facilitate fast AI model downloading to end users. By caching models on edge servers, mobile networks can deliver models to end user…
AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks
Zheng Lin, Guanqiao Qu, Wei Wei +2
The increasing complexity of deep neural networks poses significant barriers to democratizing them to resource-limited edge devices. To address this challenge, split federated lear…
Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities
Zheng Lin, Guanqiao Qu, Qiyuan Chen +3
Large language models (LLMs), which have shown remarkable capabilities, are revolutionizing AI development and potentially shaping our future. However, given their multimodality, t…
Optimal Resource Allocation for U-Shaped Parallel Split Learning
Song Lyu, Zheng Lin, Guanqiao Qu +3
Split learning (SL) has emerged as a promising approach for model training without revealing the raw data samples from the data owners. However, traditional SL inevitably leaks lab…
Split Learning in 6G Edge Networks
Zheng Lin, Guanqiao Qu, Xianhao Chen +1
With the proliferation of distributed edge computing resources, the 6G mobile network will evolve into a network for connected intelligence. Along this line, the proposal to incorp…
Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities
Xianhao Chen, Yiqin Deng, Haichuan Ding +4
Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of ap…