5 papers
Improving Convergence for Semi-Federated Learning: An Energy-Efficient Approach by Manipulating Over-the-Air Distortion
Jingheng Zheng, Hui Tian, Wanli Ni +2
In this paper, we propose a hybrid learning framework that combines federated and split learning, termed semi-federated learning (SemiFL), in which over-the-air computation is util…
Token Communication in the Era of Large Models: An Information Bottleneck-Based Approach
Hao Wei, Wanli Ni, Wen Wang +3
This letter proposes UniToCom, a unified token communication paradigm that treats tokens as the fundamental units for both processing and wireless transmission. Specifically, to en…
Federated Intelligence: When Large AI Models Meet Federated Fine-Tuning and Collaborative Reasoning at the Network Edge
Wanli Ni, Haofeng Sun, Huiqing Ao +1
Large artificial intelligence (AI) models exhibit remarkable capabilities in various application scenarios, but deploying them at the network edge poses significant challenges due…
Reconfigurable Intelligent Surface for Internet of Robotic Things
Wanli Ni, Ruyu Luo, Xinran Zhang +3
With the rapid development of artificial intelligence, robotics, and Internet of Things, multi-robot systems are progressively acquiring human-like environmental perception and und…
Energy-Efficient Robust Beamforming for Multi-Functional RIS-Aided Wireless Communication under Imperfect CSI
Ailing Zheng, Wanli Ni, Wen Wang +2
The robust beamforming design in multi-functional reconfigurable intelligent surface (MF-RIS) assisted wireless networks is investigated in this work, where the MF-RIS supports sig…