Shujie Yang

About

Shujie Yang is a leading researcher at the forefront of the Internet of Robotic Things (IoRT), federated learning security, and cooperative crowdsourcing systems. His work addresses critical challenges in the digital economy era, where robotic networks must balance task complexity with constrained resources. Yang’s most cited paper, “Task-Driven Cooperative Internet of Robotic Things Crowdsourcing” (2024, 33 citations), introduces a hierarchical game-theoretic framework that optimizes resource allocation and collaboration among robot nodes, a foundational contribution to scalable IoRT crowdsourcing. He further advances the field with “Safeguarding Privacy and Integrity of Federated Learning in Heterogeneous Cross-Silo IoRT Environments” (2024, 14 citations), pioneering a moving target defense approach that protects federated learning from adversarial threats in diverse robotic ecosystems. In his recent work, “SecFFT: Safeguarding Federated Fine-Tuning for Large Vision Language Models Against Covert Backdoor Attacks in IoRT Networks” (2024, 7 citations), Yang tackles emerging vulnerabilities in large vision-language models, ensuring robust visual perception for smart city, factory, and transportation applications. With cumulative citations exceeding 50 within a single year, Yang’s research is rapidly shaping secure, efficient, and intelligent robotic networks, making him a rising authority in IoRT security and cooperative autonomy.

Research Focus

Key Achievements

Top Papers

1. 1
   Task-Driven Cooperative Internet of Robotic Things Crowdsourcing: From the Perspective of Hierarchical Game Theoretic

   33 citations · 2024
2. 2
   Safeguarding Privacy and Integrity of Federated Learning in Heterogeneous Cross-Silo IoRT Environments: A Moving Target Defense Approach

   14 citations · 2024
3. 3
   SecFFT: Safeguarding Federated Fine-Tuning for Large Vision Language Models Against Covert Backdoor Attacks in IoRT Networks

   7 citations · 2024

Key Collaborators

Contact & Links