6 papers
Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs
Xiaowen Cao, Zhonghao Lyu, Shicheng Chu +6
The increasing scale and computational demands of large artificial intelligence models (LAIMs) present significant challenges for efficient inference in resource-constrained distri…
Sense Smarter, Think Better: A Survey on Edge Perception for Next-Generation Networks
Zhonghao Lyu, Xiaowen Cao, Xianxin Song +11
Edge perception has emerged as a foundational capability for future wireless networks, enabling the network edge to proactively sense, interpret, and interact with the physical env…
FlexSpec: Frozen Drafts Meet Evolving Targets in Edge-Cloud Collaborative LLM Speculative Decoding
Yuchen Li, Rui Kong, Zhonghao Lyu +11
Deploying large language models (LLMs) in mobile and edge computing environments is constrained by limited on-device resources, scarce wireless bandwidth, and frequent model evolut…
Closing the Generalization Gap in Parameter-efficient Federated Edge Learning
Xinnong Du, Zhonghao Lyu, Xiaowen Cao +3
Federated edge learning (FEEL) provides a promising foundation for edge artificial intelligence (AI) by enabling collaborative model training while preserving data privacy. However…
Empowering Intelligent Low-altitude Economy with Large AI Model Deployment
Zhonghao Lyu, Yulan Gao, Junting Chen +4
Low-altitude economy (LAE) represents an emerging economic paradigm that redefines commercial and social aerial activities. Large artificial intelligence models (LAIMs) offer trans…
The Larger the Merrier? Efficient Large AI Model Inference in Wireless Edge Networks
Zhonghao Lyu, Ming Xiao, Jie Xu +2
The growing demand for large artificial intelligence model (LAIM) services is driving a paradigm shift from traditional cloud-based inference to edge-based inference for low-latenc…