Papers
1
Total Citations
564
H-Index
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About
Haomiao Yang is a leading researcher at the intersection of privacy-preserving machine learning and industrial artificial intelligence. His work addresses a critical challenge in the era of Industry 4.0: how to harness the power of deep learning for sensitive industrial data without compromising privacy. His most-cited paper, "Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence" (2019, 564 citations), is a foundational contribution that demonstrates how federated learning can be adapted for real-world industrial applications. By designing protocols that protect data confidentiality while maintaining model accuracy and efficiency, Yang has enabled collaborative AI training across decentralized, privacy-sensitive environments—a key enabler for smart manufacturing and cyber-physical systems. His research has had substantial impact, with his top-cited work alone garnering over 560 citations, reflecting its influence on both academia and industry. Yang’s contributions are particularly notable for bridging theoretical privacy guarantees with practical deployment constraints, making him a pivotal figure in the advancement of secure and scalable AI for industrial settings.
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