Dr Bing Luo
Dr Bing Luo is an Assistant Professor of Data and Computational Science at Duke Kunshan University (DKU), a U.S.–China partnership between Duke University and Wuhan University. Before joining DKU, Dr Luo was a joint postdoctoral researcher at The Chinese University of Hong Kong, Shenzhen, and Yale University. He earned his PhD from The University of Melbourne after several years of industry experience as a project manager at China Mobile Corporation Headquarters. He is a Senior Member of IEEE.
Dr. Luo leads the Edge Intelligence Lab at DKU, where his research focuses on the theory and application of federated learning, edge intelligence, and LLM-based agentic systems. His work has appeared in leading venues including IEEE Journal on Selected Areas in Communications (JSAC), IEEE Transactions on Communications (TCOM), IEEE Transactions on Mobile Computing (TMC), IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE INFOCOM, IEEE ICDCS, and ACM MobiHoc. His team developed and open-sourced FedKit, the world’s first cross-platform on-device federated learning framework for both Android and iOS, which has been deployed in FedCampus, DKU’s health data privacy application platform. His team recently developed and launched ChatDKU (chatdku.dukekunshan.edu.cn), a RAG-agent AI chatbot designed for the DKU community.
Select publications
- B. Luo, W. Xiao, S. Wang, J. Huang, L. Tassiulas, “Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks,” in IEEE Transactions on Mobile Computing (TMC), vol. 23, no. 10, pp. 9663-9677, Oct. 2024
- B. Luo, X. Ouyang, P. Sun, P. Han, N, Ding, J. Huang, “Optimization Design for Federated Learning in Heterogeneous 6G Networks,” in IEEE Network, vol. 37, no. 2, pp. 38-43, March 2023
- B. Luo, PL. Yeoh, R. Schober and B. Krongold, “Distributed Energy Beamforming for Wireless Power Transfer over Frequency-Selective Fading Channels,” IEEE Transactions on Green Communications and Networking (TGCN), vol. 6, no. 4, pp. 2100-2114, Dec. 2022
- B. Luo, X. Li, S. Wang, J. Huang, L. Tassiulas, “Cost-Effective Federated Learning in Mobile Edge Networks,” IEEE Journal on Selective Areas in Communications (JSAC), 39 (12): 3606-3621. 2021.
- B. Luo, PL. Yeoh, and B. Krongold, “Optimal Co-Phasing Power Allocation and Capacity of Coordinated OFDM Transmission with Total and Individual Power Constraints,” IEEE Transactions on Communications (TCOM), vol. 67, no. 10, pp. 7103-7113, Oct. 2019.
- B. Luo, Y. Feng, S. Wang, J. Huang, L. Tassiulas, “Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation,” in Proc. of IEEE International Conference on Distributed Computing Systems (ICDCS), July 2023.
- B. Luo, W. Xiao, S. Wang, J. Huang, L. Tassiulas, “Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling,” in Proc. of IEEE International Conference on Computer Communications (INFOCOM), May 2022.
- B. Luo, X. Li, S. Wang, J. Huang, L. Tassiulas, “Cost-Effective Federated Learning Design,” in Proc. of IEEE International Conference on Computer Communications (INFOCOM), May 2021.
Further links
Dr Luo’s webpage: https://luobing1008.github.io/
