Guowen Xu

University of Electronic Science and Technology of China, Nanyang Technological University

Papers

Total Citations

599

H-Index

About

Guowen Xu is a leading researcher at the intersection of cybersecurity, privacy, and artificial intelligence, with a primary focus on securing intelligent systems in industrial and robotic contexts. His seminal work, "Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence" (2019, 564 citations), pioneered privacy-preserving machine learning frameworks for Industry 4.0, addressing the critical challenge of training AI models on sensitive industrial data without centralized collection. This foundational contribution has shaped how federated learning is deployed in privacy-sensitive industrial environments. Xu has also made significant advances in robotic security, notably exposing critical vulnerabilities in the Robot Operating System 2 (ROS2) in his 2022 study (34 citations), which revealed fundamental security flaws in the widely adopted robotic middleware. His most recent work (2025) introduces innovative adversarial patch techniques for privacy management in commercial robot vacuum cleaners, demonstrating practical defenses against inadvertent privacy breaches in smart home devices. Through these contributions, Xu has established himself as a key figure in developing secure, privacy-aware AI systems for real-world industrial and consumer applications.