Wei Liang

Papers

Total Citations

181

H-Index

About

Wei Liang is a researcher at the forefront of distributed intelligence, privacy-preserving computing, and collaborative robotic systems. His work bridges the rapidly evolving fields of federated learning, mobile robotics, and smart manufacturing, addressing critical challenges in deploying AI across decentralized, resource-constrained environments. Liang's most influential contribution, "Decentralized P2P Federated Learning for Privacy-Preserving and Resilient Mobile Robotic Systems" (2023), has garnered an impressive 169 citations, underscoring its significance to the research community. This work advances federated learning frameworks tailored for swarms of mobile robots operating in 5G and beyond networks, tackling the dual challenges of data privacy and system resilience in real-world smart industry deployments. By eliminating reliance on centralized servers, his peer-to-peer approach represents a meaningful architectural leap for distributed machine learning. Complementing this, his research on intelligent containment control for cloud-based collaborative manufacturing demonstrates a broader commitment to optimizing multi-robot cooperation and automated efficiency in modern production environments. Together, these contributions position Wei Liang as an emerging voice in the intersection of edge intelligence, robotics, and secure distributed systems — research areas of growing importance as industries worldwide accelerate their adoption of autonomous, connected technologies.