Wei-Bin Kou
Papers
1
Total Citations
28
H-Index
1
About
Wei-Bin Kou is a leading researcher at the intersection of artificial intelligence, federated learning, and smart construction. His work focuses on developing decentralized, privacy-preserving machine learning frameworks to address critical challenges in the built environment. Kou’s most cited paper, “A hierarchical federated learning framework for collaborative quality defect inspection in construction” (2024), has already garnered 28 citations, reflecting its timely impact. This work introduces a novel hierarchical architecture that enables multiple construction sites to collaboratively train defect detection models without sharing sensitive data, overcoming key barriers of data silos and communication overhead. By integrating edge computing with federated aggregation, Kou’s framework significantly improves inspection accuracy and efficiency while preserving data sovereignty. His contributions are shaping the future of intelligent construction, where AI-driven quality control can be deployed at scale across distributed project sites. Kou’s research not only advances technical frontiers in distributed learning but also offers practical solutions for reducing costly rework and enhancing safety in construction. His work stands as a cornerstone for researchers and practitioners seeking to harness federated learning for real-world industrial applications.
Research Focus
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Top Papers
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