Xiaokang Zhou
Papers
1
Total Citations
169
H-Index
1
About
Xiaokang Zhou is a leading researcher at the intersection of federated learning, edge intelligence, and privacy-preserving systems for next-generation mobile robotics. His most cited work, "Decentralized P2P Federated Learning for Privacy-Preserving and Resilient Mobile Robotic Systems" (2023, 169 citations), addresses a critical challenge in modern smart industry: enabling swarms of mobile robots to collaboratively learn from distributed data without compromising privacy or relying on a central server. This contribution is foundational for deploying resilient, scalable machine learning in 5G and beyond, where communication constraints and security are paramount. Zhou’s research advances decentralized learning paradigms that balance efficiency, robustness, and data confidentiality, directly impacting autonomous systems and industrial IoT. His work is widely recognized for bridging theoretical frameworks with practical deployment in dynamic, resource-limited environments. With a growing citation footprint, Zhou continues to shape how intelligent systems learn and cooperate in the real world, making him a key figure in the evolution of privacy-aware, distributed AI for robotics.
Research Focus
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Top Papers
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