116 citations · 210 across the 41 of their papers we have counts for
11 papers · 1 filter
Siamese Machine Unlearning with Knowledge Vaporization and Concentration
Songjie Xie, Hengtao He, Shenghui Song +2
In response to the practical demands of the ``right to be forgotten" and the removal of undesired data, machine unlearning emerges as an essential technique to remove the learned k…
AI and Deep Learning for Terahertz Ultra-Massive MIMO: From Model-Driven Approaches to Foundation Models
Wentao Yu, Hengtao He, Shenghui Song +4
This study explored the transformative potential of artificial intelligence (AI) in addressing the challenges posed by terahertz ultra-massive multiple-input multiple-output (UM-MI…
Toward Real-Time Edge AI: Model-Agnostic Task-Oriented Communication with Visual Feature Alignment
Songjie Xie, Hengtao He, Shenghui Song +2
Task-oriented communication presents a promising approach to improve the communication efficiency of edge inference systems by optimizing learning-based modules to extract and tran…
Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks
Tianqu Kang, Zixin Wang, Hengtao He +3
Fine-tuning large pre-trained foundation models (FMs) on distributed edge devices presents considerable computational and privacy challenges. Federated fine-tuning (FedFT) mitigate…
Fast Adaptation for Deep Learning-based Wireless Communications
Ouya Wang, Hengtao He, Shenglong Zhou +4
The integration with artificial intelligence (AI) is recognized as one of the six usage scenarios in next-generation wireless communications. However, several critical challenges h…
Low-Complexity CSI Feedback for FDD Massive MIMO Systems via Learning to Optimize
Yifan Ma, Hengtao He, Shenghui Song +2
In frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems, the growing number of base station antennas leads to prohibitive feedback overhead for dow…