4 citations · 7 across the 6 of their papers we have counts for
7 papers
TPI-LLM: Serving 70B-scale LLMs Efficiently on Low-resource Edge Devices
Zonghang Li, Wenjiao Feng, Mohsen Guizani +1
Large model inference is shifting from cloud to edge due to concerns about the privacy of user interaction data. However, edge devices often struggle with limited computing power,…
Log2graphs: An Unsupervised Framework for Log Anomaly Detection with Efficient Feature Extraction
Caihong Wang, Du Xu, Zonghang Li
In the era of rapid Internet development, log data has become indispensable for recording the operations of computer devices and software. These data provide valuable insights into…
HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse
Shenglai Zeng, Zonghang Li, Hongfang Yu +4
Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, is a promising approach to enable edge intelligence in the emerging Indus…
Personalized Saliency in Task-Oriented Semantic Communications: Image Transmission and Performance Analysis
Jiawen Kang, Hongyang Du, Zonghang Li +4
Semantic communication, as a promising technology, has emerged to break through the Shannon limit, which is envisioned as the key enabler and fundamental paradigm for future 6G net…
Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT
Zonghang Li, Yihong He, Hongfang Yu +4
Nowadays, the industrial Internet of Things (IIoT) has played an integral role in Industry 4.0 and produced massive amounts of data for industrial intelligence. These data locate o…
Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training
Shenglai Zeng, Zonghang Li, Hongfang Yu +4
Federated learning (FL) is a rapidly growing privacy-preserving collaborative machine learning paradigm. In practical FL applications, local data from each data silo reflect local…