Papers
1
Total Citations
114
H-Index
1
About
Xuanyu Cao is a leading researcher at the intersection of distributed machine learning, wireless communications, and network intelligence. His work addresses the fundamental challenge of enabling efficient, scalable, and privacy-preserving learning across decentralized systems—a critical need for next-generation intelligent networks. His most-cited paper, “Communication-Efficient Distributed Learning: An Overview” (2023), with 114 citations, provides a comprehensive framework for reducing the communication overhead in collaborative model training among mobile devices, robots, and sensors. This overview has become a key reference for researchers tackling the bandwidth and latency constraints of real-world distributed systems. Beyond this survey, Cao has made significant contributions to optimizing communication protocols for federated learning and multi-agent reinforcement learning, often bridging theory and practical system design. His work is widely recognized for its impact on the development of scalable AI for edge computing and IoT networks, earning him a reputation as a thought leader in communication-efficient learning. With a growing citation record, Cao continues to shape how intelligent agents learn collaboratively in resource-constrained environments.
Research Focus
Key Achievements
Top Papers
- 1Communication-Efficient Distributed Learning: An Overview114 citations · 2023