Bing Luo (罗冰)

Bing Luo Assistant Professor of Data and Computational Science, Duke Kunshan University Email: bl291 [at] duke.edu Biography： I am a Tenure-Track Assistant Professor of Data and Computational Science at Duke Kunshan University (DKU). I received my Ph.D. degree from The University of Melbourne, and prior to joining DKU, I was a Postdoctoral Researcher at CUHK(SZ) and Yale University. Before academia, I was a Project Manager at China Mobile Corporation Headquarter, Beijing. I was a visiting scholar at Yale University, Friedrich-Alexander-University Erlangen-Nuremberg, Aalto University, and IBM T. J. Watson Research Center. I am a senior member of IEEE. Research Interests： Federated learning and analytics, AI-powered mobile and IoT systems, 5G/6G wireless communications, network optimization and game theory.

Recruiting

News

[May. 2025] Awarded the DKU-Duke Faculty Collaborative Scholarship Grant.

[Mar. 2025] Our survey paper on "Federated Foundation Models" has been accepted to IEEE Transactions on Knowledge and Data Engineering (TKDE). This is a joint work with multiple universities and led by WeBank.

[Mar. 2025] Our paper on "Strategic Prompt Pricing for AIGC Services" got accepted to WiOpt 2025. This is a joint work with my collaborators at CUHKSZ, led by my long-term co-supervised PhD student Xiang Li.

[Mar. 2025] Our tutorial proposal “Enabling On-Device Federated Learning at the Edge: Theory, Mechanisms, Systems, and Applications” got accepted at IEEE ICDCS 2025.

[Feb. 2025] Our tutorial proposal“Advancing Federated Learning in Practice: From Theory to Real-World Edge Applications”got accepted at IEEE ICMLCN 2025.

[Jan. 2025] Awarded THE DUKE UNIVERSITY PROVOST FUND.

Research Highlights

ChatDKU: A Powerful RAG-Agent Chatbot tailored for DKU We are pleased to introduce ChatDKU, an innovative AI assistant tailored for Duke Kunshan University (DKU). Utilizing a RAG-Agent architecture and an automatically updated database, ChatDKU efficiently provides accurate responses to student, faculty and stuff inquiries, covering academic and administrative needs while ensuring data privacy through private deployment. ChatDKU features innovative designs to shorten Agent response times, develop an API-friendly architecture, and implement effective access management. It also ensures convenient user access, enhancing user experience while maintaining security and efficiency. Now we are testing ChatDKU-advising, a version specifically designed to support academic advising at DKU. It aims to assist students by providing quick access to advising-related information and guidance, while also helping faculty respond more efficiently to common student inquiries. Now you can visit ChatDKU: https://chatdku.dukekunshan.edu.cn. Looking forward to your feedback! This initiative is led by Professor Bing Luo, with undergraduate students Mingxi Li, Yuxiang Lin, Ningyuan Yang, Qin Chen Shuhao, Guanglei Cheng, Sean Allen Siegfreid Bugarin, Munish P. Lohani, Anar, Temuulen, Yihang Zou.

FedKit: Enabling Cross-Platform Federated Learning for Android and iOS We present FEDKIT, which pipelines Cross-Platform FL for Android and iOS development by enabling model conversion, hardware-accelerated training, and cross-platform model aggregation. Our workflow supports flexible machine learning operations (MLOps) in production, facilitating continuous model delivery and training. This is a collaborative project with my supervised DKU undergraduate students Sichang He (lead), Beilong Tang, and Boyan Zhang, PhD student Jiaqi Shao, as well as collaborators Xiaomin Ouyang (UCLA) and Daniel Nata (Flower). Our work has been ACCEPTED at IEEE INFOCOM 2024 Demo.

FedCampus: A Privacy-Preserving Data Platform for Smart Campus We're excited to announce the launch of the FedCampus Project - a privacy-preserving smart campus application, available on Android and iOS. Video online available. This app implements two key privacy-preserving technologies: Federated Learning and Differential Privacy. Check out our 100 customized smart watches for participants at DKU and FedCampus APP. This is a collaborative project with DKU undergraduate students: Sichang He, Beilong Tang, Boyan Zhang, Luyao Wang, Renyuan Zhang, and DKU/Duke ECE Graduate student Qingning Zeng, as well as my PhD student Jiaqi Shao. We presented our FedCampus and FedKit projects at the Flower AI Summit 2024, one of the world's largest Federated Learning conference, in London, UK. Our work has been ACCEPTED at ACM MobiHoc 2024 Demo.

Optimization and System Development for Federated Learning in Mobile and IoT Networks This is a collaborative research project with Prof. Jianwei Huang, Prof. Leandros Tassiulas, and Dr. Shiqiang Wang (IBM T. J. Watson Research Center). I lead a small team of graduate (Xiang Li) and undergraduate (Wenli Xiao, Jiaqi Shao, Yingyi Huang, and Yutong Feng) students from CUHKSZ in developing edge-based cross-device federated learning prototypes. Check out our Fed-Mobile and Fed-IoT prototypes on the left side. Our prototype supports Mobile and IoT devices operating at WiFi and USRP-based 4G/5G wireless networks.

I. Adaptive Client Sampling for Federated Learning This sub-project aims to design optimal client sampling probability to tackle system heterogeneity (e.g., computation and communication capabilities) and statistical heterogeneity (non-i.i.d. and unbalanced data) for fast convergence with respect to wall-clock time. Our paper “Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling” published in IEEE INFOCOM, 2022 Our paper "Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks" published at IEEE Transactions on Mobile Computing (TMC),2024.

II. Cost-Effective Federated Learning Design This sub-project aims to minimize the total FL resource cost via optimizing the key control parameters of number of local iteration steps and number of per-round participants. Our paper “Cost-Effective Federated Learning Design” published in IEEE INFOCOM, 2021. Our paper “Cost-Effective Federated Learning in Mobie Edge Networks” published in IEEE JSAC Special Issue on Distributed Learning over Wireless Edge Networks, 2021.

Game-Theoretic Incentive Mechanism for Unbiased Federated Learning Motivation: Incentive mechanism is crucial for federated learning (FL) when rational clients do not have the same interests in the global model as the server. However, due to system heterogeneity and limited budget, it is generally impractical for the server to incentivize all clients to participate in all training rounds (known as full participation). Approach: This project aims to propose a game-theoretic incentive mechanism for FL with randomized client participation, where the server adopts a customized pricing strategy that motivates different clients to join with different participation levels (probabilities) for obtaining an unbiased and high-performance model. Our paper “"Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation" got accepted in IEEE ICDCS 2023. Our paper "Optimal Mechanism Design for Heterogeneous Client Sampling in Federated Learning" got accepted in IEEE Transactions on Mobile Computing (TMC),2024.

Federated Reinforcement Learning for Robotics Our paper "FedRos - Federated Reinforcement Learning for Networked Mobile-Robot Collaboration" has been accepted by ICDCS 2023 Demo and Poster program. My research project “Federated Learning for Multi-Robotics Cooperation” got funded with 500,000rmb from AIRS! (July. 2021-June. 2022). I lead this project with two excellent research interns Wenli Xiao and Tingwei Ye from CUHK(SZ). Check out our developed demo and cool NAO robots on the left side. I participated in the WeBank-AIRS collaboration project “Federated Reinforcement Learning”, led by Prof. Qiang Yang. (Nov. 2020-Oct. 2022)

Resource Optimization for Distributed Coordinated Multi-Point (CoMP) Systems This project aims to design optimal resource allocation strategies for improving the capacity of coherent and non-coherent CoMP systems over multiple wireless channels under hybrid power constraints. (Collaborated with Assoc. Prof. Brian Krongold and Dr. Phee Lep Yeoh) Representative publications: IEEE ICC 2017, IEEE GLOBECOM 2019, IEEE TCOM 2019, IEEE TGCN 2021

Resource Optimization for Distributed Cooperative Wireless Energy Harvesting Systems This project aims to design optimal energy beamforming strategies for improving the efficiency of distributed cooperative wireless power transfer (WPT) systems under hybrid power constraints. (Collaborated with Prof. Robert Schober, Assoc. Prof. Brian Krongold and Dr. Phee Lep Yeoh) Representative publications: ICC 2019, IEEE GLOBECOM 2019, IEEE TGCN 2022

Publications (Note: Student co-authors (co-)supervised by me are underlined)

Journal Papers

• T. Fan, et al. "Ten Challenging Problems in Federated Foundation Models," in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2025

• C. Hu, N. Wu, X. Liu, B. Luo, Y. Wang, J. Jiang, D. Cheng, “PriFairFed: A Local Differentially Private Federated Learning Algorithm for Client-Level Fairness,”accepted in IEEE Transactions on Mobile Computing (TMC), 2024

• G. Liao^‡, B. Luo^‡, Y. Feng, M. Zhang*, X. Chen, “Optimal Mechanism Design for Heterogeneous Client Sampling in Federated Learning” accepted in IEEE Transactions on Mobile Computing (TMC), 2024

• B. Luo, W. Xiao, S. Wang, J. Huang, L. Tassiulas, “Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks,”accepted in IEEE Transactions on Mobile Computing (TMC), 2024.

• B. Luo, P. Han, P. Sun, X. Ouyang, J. Huang and N. Ding, "Optimization Design for Federated Learning in Heterogeneous 6G Networks," in IEEE Network, vol. 37, no. 2, pp. 38-43, March/April 2023

• B. Luo, PL. Yeoh, R. Schober and B. Krongold, “Distributed Energy Beamforming for Wireless Power Transfer over Frequency-Selective Fading Channels,” in IEEE Transactions on Green Communications and Networking, vol. 6, no. 4, pp. 2100-2114, Dec. 2022.

• B. Luo, PL. Yeoh, and B. Krongold, “Structural Properties of Optimal Power Allocation for DAS-OFDM under Joint Total and Individual Power Constraints,” IEEE Transactions on Green Communications and Networking, vol. 6, no. 1, pp. 530-542, March 2022.

• B. Luo, X. Li, S. Wang, J. Huang and L. Tassiulas, "Cost-Effective Federated Learning in Mobile Edge Networks," in IEEE Journal on Selected Areas in Communications, vol. 39, no. 12, pp. 3606-3621, Dec. 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, vol. 67, no. 10, pp. 7103-7113, Oct. 2019.

• B. Luo, Q. Cui, and X. Tao, “Optimal Joint Water-Filling for Coordinated Transmission over Frequency-Selective Fading Channels,” IEEE Communication Letters, vol.15, no.2, pp.190-192, Feb. 2011.

• B. Luo, Q. Cui, X. Tao, and P. Zhang, “Closed Form Solutions of Joint Water-Filling for Coordinated Transmission,” IEICE Transactions on Communications, vol. 93-B, no. 12, pp. 3461-3468, Jan. 2010.

• Q. Cui, B. Luo, X. Huang and A.A. Dowhuszko, “Closed Form Solution for Minimizing Power Consumption in Coordinated Transmissions,” EURASIP Journal on Wireless Communications and Networking, vol. 2012, no. 122, Mar. 2012.

• Q. Cui, X. Huang, B. Luo and X. Tao, “Capacity Analysis and Optimal Power Allocation for Coordinated MIMO-OFDM Systems,” Science China Information Sciences, vol. 55, no. 6, pp.1372-1378, Jun. 2012.

Conference Papers

• X. Li, B. Luo, J. Huang, Y. Luo, "Strategic Prompt Pricing for AIGC Services: A User-Centric Approach," in International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), 2025

• B. Zhang, C. Qin, B. Luo,“Demo: Privacy-Preserving Room Occupancy Estimation Using Federated Analytics of BLE Packets” in ACM Conference on Embedded Networked Sensor Systems (SenSys), Nov. 2024.

• R. Zhou, L. Qu, L. Zhang, Z. Li, H. Yu, B. Luo, "Fed-MUnet: Multi-modal Federated Unet for Brain Tumor Segmentation" in Proc. IEEE International Conference on E-health Networking, Application & Services (Healthcom), Nov. 2024.

• J. Geng, B. Tang, B. Zhang, J. Shao, B. Luo, “Demo: FedCampus: A Real-world Privacy-preserving Mobile Application for Smart Campus via Federated Learning & Analytics” in Proc. ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), Oct. 2024.

• X. Li, Y. Luo, B. Luo, J. Huang, “Social Welfare Maximization for Federated Learning with Network Effects," in Proc. ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), Oct. 2024.

• Y. Gong, B. Luo, C. Hu, D. Cheng, "An Overlapping Coalition Game for Individual Utility Maximization in Federated Learning," in FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics, in Conjunction with ACM SIGKDD, Aug. 2024

• Z. He, T. Tu, KY. Wang, B. Luo, D. Cheng, C. Hu,“Federated Spectrum Management Through Hedonic Coalition Formation,”in Proc. Asia-Pacific Workshop on Networking (APNet), Aug, 2024

• S. Wang, B. Luo, M. Tang, “Tackling System-Induced Bias in Federated Learning: A Pricing-based Incentive Mechanism,” Proc. IEEE International Conference on Distributed Computing Systems (ICDCS), Jul. 2024.

• J. Shao, T. Lin, X. Cao, B. Luo, “Federated Unlearning: a Perspective of Stability and Fairness,” in Privacy Regulation and Protection in Machine Learning Workshop, in Conjunction with ICLR 2024 (PML-ICLR' 24), May, 2024

• W. Zhu, J. Jia, B. Luo, X. Lin,“Federated Unlearning with Multiple Client Partitions” in IEEE International Conference on Communications (ICC) Jun. 2024

• J. Geng, Y. Hou, X. Tao, J. Wang, B. Luo, “Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Sampling,”in IEEE International Conference on Communications (ICC), Jun. 2024

• S. He, B. Tang, B. Zhang, J. Shao, X. Ouyang, D. Nata, B. Luo, “Demo: FedKit: Enabling Cross-Platform Federated Learning for Android and iOS,” in IEEE International Conference on Computer Communications (INFOCOM), May 2024.

• Y. Chen, W. Xu, X. Wu, M. Zhang, B. Luo, “Personalized Local Differentially Private Federated Learning with Adaptive Client Sampling,” in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Apr. 2024.

• W. Xiao, T. Ye, B. Luo, J. Huang, "FedRos - Federated Reinforcement Learning for Networked Mobile-Robot Collaboration", accepted in Proc. IEEE International Conference on Distributed Computing Systems (ICDCS) Poster and Demo Session, Jul. 2023.

• B. Luo, Y. Feng, S. Wang, J. Huang, L. Tassiulas, “Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation”, accepted in Proc. IEEE International Conference on Distributed Computing Systems (ICDCS), 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. IEEE International Conference on Computer Communications (INFOCOM), 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), 2021.

• B. Luo, PL. Yeoh, R. Schober and B. Krongold, “Optimal Frequency-Selective Energy Beamforming with Joint Total and Individual Power Constraints”, in Proc. IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, December, 2019.

• B. Luo, PL. Yeoh and B. Krongold, “Optimal Power Allocation for DAS-OFDM under Joint Total and Individual Power Constraints,” in Proc. IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, December, 2019.

• B. Luo, PL. Yeoh, R. Schober and B. Krongold, “Optimal Energy Beamforming for Distributed Wireless Power Transfer over Frequency-Selective Channels,” in Proc. IEEE International Conference on Communications (ICC), Shanghai, China, May 2019.

• B. Luo, PL. Yeoh, and B. Krongold, “Optimal Co-phasing Power Allocation for Coordinated OFDM Transmission,” in Proc. IEEE International Conference on Communications (ICC), Paris, France, Jun. 2017.

• B. Luo, Q. Cui, X. Tao, and A.A. Dowhuszko, “On the Optimal Power Allocation for Coordinated Wireless Backhaul in OFDM Based Relay Systems,” in Proc. IEEE International Conference on Communications (ICC), Budapest, Hungary, Jun. 2013.

• B. Luo, Q. Cui, and X. Tao, “Constant-Power Joint Water-filling for Coordinated Transmission,” in Proc. IEEE Global Communications Conference (GLOBECOM), Houston, US, Dec. 2011.

• B. Luo, Q. Cui, H. Wang, and X. Tao, “Optimal Joint Water-filling for OFDM Systems with Multiple Cooperative Power Sources,” in Proc. IEEE Global Communications Conference (GLOBECOM), Miami, US, Dec. 2010.

• Q. Cui, B. Luo, and X. Huang, “Joint Power Allocation Solutions for Power Consumption Minimization in Coordinated Transmission System,” in Proc. IEEE Global Communications Conference (GLOBECOM) Workshop on Multi-Cell Cooperation, Houston, US, Dec. 2011.

Teaching

Undergraduate Course

• COMPSCI 204: Introduction to Artificial Intelligence (Spring 2025).

• CS 101: Introduction to Computer Science (Fall 2022, 2023, 2024, Spring 2024).

• CS 401: Cloud Computing (Spring 2023).

Graduate Course

• ECE 586: Vector Space Methods with Applications (Spring 2023, 2024, 2025).

Group

Principal Investigator (PI): Dr. Bing Luo, Assistant Professor of Data and Computational Science, Duke Kunshan University

Current Group Members

• Jiaqi Shao – PhD Student (BS: The Chinese University of Hong Kong, Shenzhen)

• Mingxi Li – PhD Student (BS: Wuhan University)

• Jiaxiang Geng – PhD Student (BS & MS: Beijing University of Posts and Telecommunications)

• Ran Wang – PhD Student (BS: Fudan University; MS: New York University)

• Hongtao Chen – Research Assistant (BS: Jinan University; MS: University of Manchester)

Previously Supervised UG Students

• Wenli Xiao – MS at Carnegie Mellon University

• Ziqi Li – MS at University of Oxford

• Sichang He – PhD at University of Southern California

• Renzo Balcazar – PhD at The Hong Kong University of Science and Technology, Guangzhou

• Beilong Tang – PhD at North Carolina State University

• Binyan Sun – MS at Columbia University

• Chenshuhao Qin - MS at Duke University

• Aicha Slaitane - RA at MBZUAI

Services

Tutorial

• Enabling On-Device Federated Learning at the Edge: Theory, Mechanisms, Systems, and Applications (IEEE ICDCS 2025)

• Advancing Federated Learning in Practice: From Theory to Real-World Edge Applications (IEEE ICMLCN 2025)

Conference (Technical) Program Committee Member

• ICML'25 Collaborative & Federated Agentic Workflows

• ICLR’25 SynthData Workshop

• ICML 2025

• ACM MobiHoc 2025

• AISTATS 2025

• NeurIPS 2024

• ECAI 2024,2025

• KDD - Federated Learning Workshop 2024, 2025

• IEEE ICC 2020-2025

• IEEE GLOBECOM 2020-2025

• IEEE ICDCS 2023,2024

• IEEE EDGE 2024 (senior PC)

• ICLR-Privacy Regulation and Protection in Machine Learning Workshop 2024

• NeurIPS - Federated Learning Workshop 2022,2023

• ICML- Federated Learning Workshop 2021, 2023

• AAAI- Federated Learning Workshop 2022

• IJCAI- Federated Learning Workshop 2022

Journal Reviewer

• IEEE Journal on Selected Areas in Communications (JSAC)

• IEEE Transactions on Communications (TCOM)

• IEEE Transactions on Wireless Communications (TWC)

• IEEE/ACM Transactions on Networking (ToN)

• IEEE Transactions on Mobile Computing (TMC)

• IEEE Transactions on Green Communications and Networking (TGCN)

• IEEE Transactions on Cognitive Communications and Networking (TCCN)

• IEEE Transactions on Network Science and Engineering (TNSE)

• IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

• ACM Transactions on Intelligent Systems and Technology (TIST)

Organizing Committee Member

• Co-Organizer of Tech for Good: Economic and Computational Advances in Distributed Systems Workshop at IEEE ICDCS 2024

Selected Awards

• Joint Postdoctoral Fellowship, AIRS, CUHK(SZ) and Yale University (2020-2022)

• Melbourne Research Scholarship, Melbourne University (2016-2019)

• Kenneth Myers Memorial Scholarship, Melbourne University (2018-2019) (only one recipient every two years)

• Robert Bage Memorial Scholarship, Melbourne University (2017)

• Technical Innovation Expert Award, China Mobile (2015)

• Advanced LTE Technology Professionals Award, China Mobile (2014)

• Wu Tong Communications. Co., Ltd, Scholarship (2012)

• National First Grade Scholarship (2011)

• Meritorious Winner 24th US Mathematical Contest in Modeling (2008)