Xiao Zeng

Papers

Total Citations

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About

Xiao Zeng is a leading researcher in edge intelligence and distributed machine learning, with a focus on enabling collaborative deep learning across resource-constrained devices like smartphones, drones, and IoT sensors. His most-cited work, "Mercury" (2021, 42 citations), addresses a critical challenge in this domain: how to efficiently train deep learning models on edge devices using locally collected data while preserving privacy and minimizing communication overhead. By pioneering novel frameworks for decentralized model training, Zeng has helped bridge the gap between centralized cloud computing and the growing demand for on-device intelligence. His research has significant implications for real-time applications in autonomous systems, smart cities, and healthcare monitoring, where low-latency, privacy-preserving AI is essential. With a strong publication record and growing citation impact, Xiao Zeng is recognized for advancing the practical deployment of machine learning at the edge, making him a notable contributor to the future of distributed AI systems.