Papers
6
Total Citations
213
H-Index
5
About
Qun Jin is a leading researcher in decentralized artificial intelligence, multi-robot systems, and privacy-preserving machine learning. His most impactful contribution is the development of decentralized peer-to-peer federated learning for mobile robotic systems, a paradigm that enables swarms of robots to collaboratively learn from distributed data without compromising privacy or resilience. This work, published in 2023, has already garnered 169 citations, reflecting its significance for modern smart industry and 5G-enabled environments. Jin has also advanced cloud-based collaborative manufacturing through intelligent containment control with double constraints, addressing the challenges of coordinating multiple automated robots for efficient production. His foundational research includes probabilistic behavior and reliability analysis of multi-robot systems using Petri nets and Markov renewal process theory, as well as extended stochastic Petri net models for parallel and cooperative motions. Earlier work on enabling society with information technology and a prototype for a ubiquitous object finder in smart spaces demonstrates his long-standing commitment to practical, human-centered robotics. With a career spanning decades, Jin’s contributions are essential for building secure, scalable, and intelligent robotic systems that operate in real-world, dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Enabling Society with Information Technology13 citations · 2002
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- 5Robots in Smart Spaces - A Case Study of a u-Object Finder Prototype -5 citations · 2008
- 6