3 papers
eess.SP2026
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…
eess.SP2025
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…
cs.AI2025
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…