most citedFederated Low-Rank Adaptation with Differential Privacy over Wireless Networks

1 citations · 2 across the 10 of their papers we have counts for

collaborators

10 papers

cs.IT2025

Fluid Antenna-Assisted MU-MIMO Systems with Decentralized Baseband Processing

Tianyi Liao, Wei Guo, Hengtao He +3

The fluid antenna system (FAS) has emerged as a disruptive technology, offering unprecedented degrees of freedom (DoF) for wireless communication systems. However, optimizing fluid…

cs.IT2025

Accurate and Fast Channel Estimation for Fluid Antenna Systems with Diffusion Models

Erqiang Tang, Wei Guo, Hengtao He +3

Fluid antenna systems (FAS) offer enhanced spatial diversity for next-generation wireless systems. However, acquiring accurate channel state information (CSI) remains challenging d…

eess.SP2025

Multimodal Deep Learning-Empowered Beam Prediction in Future THz ISAC Systems

Kai Zhang, Wentao Yu, Hengtao He +3

Integrated sensing and communication (ISAC) systems operating at terahertz (THz) bands are envisioned to enable both ultra-high data-rate communication and precise environmental aw…

cs.LG2024

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…

cs.CV2024

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…

cs.LG20241 cited

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…