45 citations · 105 across the 21 of their papers we have counts for
5 papers · 1 filter
Tequila: Trapping-free Ternary Quantization for Large Language Models
Hong Huang, Decheng Wu, Rui Cen +7
Quantization techniques are essential for the deployment of Large Language Models (LLMs) on edge devices. However, prevailing methods often rely on mixed-precision multiplication t…
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…
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…
Cross-Silo Heterogeneous Model Federated Multitask Learning
Xingjian Cao, Zonghang Li, Gang Sun +2
Federated learning (FL) is a machine learning technique that enables participants to collaboratively train high-quality models without exchanging their private data. Participants u…
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…