46 citations · 105 across the 7 of their papers we have counts for
7 papers
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…
Joint Communication and Over-the-Air Computation for Semi-Federated Learning Towards Scalable AI in Computing-Heterogeneous IoT Systems
Wanli Ni, Hui Tian
The proliferation of Internet of Things (IoT) systems demands scalable artificial intelligence (AI) solutions that can operate in computing-heterogeneous environments with diverse…
Multi-Functional Reconfigurable Intelligent Surface: System Modeling and Performance Optimization
Wen Wang, Wanli Ni, Hui Tian +2
In this paper, we propose and study a multi-functional reconfigurable intelligent surface (MF-RIS) architecture. In contrast to conventional single-functional RIS (SF-RIS) that onl…
Convergence Analysis and Latency Minimization for Semi-Federated Learning in Massive IoT Networks
Jianyang Ren, Wanli Ni, Hui Tian +1
As the number of sensors becomes massive in Internet of Things (IoT) networks, the amount of data is humongous. To process data in real-time while protecting user privacy, federate…
Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning
Jingheng Zheng, Hui Tian, Wanli Ni +2
Over-the-air federated learning (AirFL) allows devices to train a learning model in parallel and synchronize their local models using over-the-air computation. The integrity of Air…