Hailiang Yang
Papers
1
Total Citations
20
H-Index
1
About
Dr. Hailiang Yang is a leading researcher at the intersection of artificial intelligence, cybersecurity, and the Internet of Medical Things (IoMT). His work focuses on making distributed machine learning systems both secure and efficient, particularly in sensitive healthcare environments. A standout contribution is his pioneering work on game-theoretic approaches to protect federated learning systems from adversarial jamming attacks, as detailed in his highly cited 2022 paper "Anti-Jamming Strategy for Federated Learning in Internet of Medical Things: A Game Approach" (20 citations). This research addresses a critical vulnerability in IoMT networks, where malicious actors can disrupt the collaborative training of AI models by targeting communication channels. By modeling the attacker-defender dynamic as a game, Dr. Yang develops optimal defense strategies that ensure model accuracy and system resilience even under attack. His work is foundational for deploying trustworthy AI in clinical settings, where data privacy and system reliability are paramount. Dr. Yang’s research continues to shape the future of secure, privacy-preserving machine learning for critical infrastructure and healthcare applications.
Research Focus
Key Achievements
Top Papers
- 1