Liqun Fu
Papers
2
Total Citations
208
H-Index
2
About
Liqun Fu is a leading researcher at the intersection of wireless communications, edge intelligence, and the Internet of Things (IoT). Her most cited work, "Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface" (2020, 194 citations), pioneers a transformative vision for shifting IoT from "connected things" to "connected intelligence." In this seminal paper, Fu proposes a novel framework that integrates federated machine learning with reconfigurable intelligent surfaces (RIS) to enable privacy-preserving, high-dimensional data analysis at the network edge. This contribution addresses critical challenges in deploying AI across distributed IoT systems, offering a scalable solution that enhances both communication efficiency and learning performance. By bridging the gap between physical-layer signal processing and distributed machine learning, Fu’s research has laid the groundwork for next-generation intelligent IoT architectures. Her work is widely cited by researchers in wireless communications, machine learning, and smart environments, reflecting its cross-disciplinary impact. Fu’s achievements highlight her as a key architect of the intelligent, adaptive wireless networks that will power future smart cities, autonomous systems, and pervasive sensing applications.
Research Focus
Key Achievements
Top Papers
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