3 papers
cs.LG2025
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…
eess.SP2025
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…
cs.LG2025
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…