Bing Luo
Biography
News
Research Highlights
Publications
Teaching
Group
Services
Awards

Bing Luo (罗冰)

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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

I have multiple postions for PhD (Degree of Wuhan University), research assistant and visiting intern.

Interested applicants (majoring in EE/CS or related) with strong mathematical and machine learning backgrounds, please email me your CV, transcript, awards, and publications (if any) at bl291@duke.edu.

News

[Nov. 2024] We are excited to announce the launch of ChatDKU — an LLM-based RAG-agent AI chatbot designed for the Duke Kunshan University (DKU) community. For more details, please refer to our demo video at Youtube, Bilibili, and research highlights.

[Oct. 2024] I will give an invited talk at the Trustworthy Federated Learning Camp organized by WeBank and SJTU in Shanghai.

[Oct. 2024] Our Demo work "Privacy-Preserving Room Occupancy Estimation Using Federated Analytics of BLE Packets" got accepted at ACM SenSys 2024 Demo Session. Congratulations to DKU undergraduate students Boyan Zhang and Chenshuhao Qin on this system project.

[Sep. 2024] I will give an invited talk in the Distributed Machine Learning Session at the 60th Annual Allerton Conference 2024.

[Aug. 2024] Our paper, "Fed-MUnet: Multi-modal Federated U-Net for Brain Tumor Segmentation," has been accepted at IEEE HealthCom 2024. Congratulations to my supervised DKU intern and undergraduate student. This is also an interdisciplinary collaboration with faculty member in Medical Physics at DKU.

[Aug. 2024] Our Demo paper "Demo: FedCampus: A Real-world Privacy-preserving Mobile Application for Smart Campus via Federated Learning & Analytics" got accepted at ACM MobiHoc 2024. Congratulations to my supervised DKU interns and undergraduate students. Demo video is available on both YouTube and Bilibili.

[July 2024] Our paper "Social Welfare Maximization for Federated Learning with Network Effects" was accepted to ACM MobiHoc 2024. This is a joint work with my collaborators at CUHKSZ, led by my long-term co-supervised PhD student Xiang Li.

[July. 2024] We organized the "Tech4Good: Economic and Computational Advances in Distributed Systems" workshop at the 44th IEEE International Conference on Distributed Computing Systems (ICDCS) 2024 in New Jersey. Schedule can be found here, where we invited two EECS Rising Stars from Cornell and TTIC to talk about Federated Learning Mechanism Design.

[June 2024] One paper got accepted to the FedKDD'24 workshop, and one got accepted to APNet'24. Both works study the Hedonic Coalition Formation Game in Federated Systems with my collaborators at Wuhan University.

[May. 2024] Received DKU-Duke Travel Grant to support my travel to Duke University in the coming year.

[May. 2024] Received Hou Tu Research (HTR) Fund from DKU Foundation, to support my interdisciplinary research with Wuhan University.

[Apr. 2024] Three of my supervised DKU undergraduate teams have won 2024 Student Innovation and Entrepreneurship (Dachuang) Project funding — two at the National level and one at the Provincial level. Congratulations to the teams!

[Apr. 2024] Our paper "Tackling System-Induced Bias in Federated Learning: A Pricing-based Incentive Mechanism" got accepted in IEEE ICDCS 2024, Federated Learning Track. This is a joint work with student and faculty at SUSTech.

[Mar. 2024] Our paper "Optimal Mechanism Design for Heterogeneous Client Sampling in Federated Learning" got accepted in IEEE Transactions on Mobile Computing (TMC). This is a joint work with SYSU and ZJU-UIUC.

[Mar. 2024] 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.

[Mar. 2024] Our paper on federated unlearning got accepted in Privacy Regulation and Protection in Machine Learning Workshop at ICLR 2024 (PML-ICLR' 24).

[Feb. 2024] My lab organized a Generative AI field trip at AWS Shanghai, with details can be found here.

[Feb. 2024] Our paper "Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks" got accepted at IEEE Transactions on Mobile Computing (TMC).

[Feb. 2024] Our work "FedKit: Enabling Cross-Platform Federated Learning for Android and iOS" got accepted at IEEE INFOCOM 2024 Demo. Congratulations to my supervised DKU undergraduate students Sichang He (lead), Beilong Tang, and Boyan Zhang on this system project. Demo video is available on both YouTube and Bilibili. Details refer to research highlights.

[Feb. 2024] Delighted to have been elevated to IEEE Senior Memeber.

[Jan. 2024] Two FL papers got accepted in ICC 2024, one on "Client Sampling in Wireless Networks"; one on "Federated Unlearning".

[Dec. 2023] One paper on "Personalized LDP for FL" got accepted in ICASSP 2024, collaborative work in supervising undergraduate students at ZJU-UIUC.

[Nov.2023] We recently launched Fedcampus Project: a privacy-preserving cross-platform (Android and iOS) smart campus application, powered by federated anlaytics and differential privacy. Details refer to research highlights.

[Oct. 2023]. My lab successfully held the 1st DKU AWS DeepRacer Self-Driving Competition, with details can be found here.

[Jun. 2023] Our paper on differential private federated analytics has been accepted by Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities(FL-ICML'23). Congratulations to my supervised CUHKSZ undergraduate student Jiaqi Shan.

[May. 2023] Our paper on federated reinforcement learning for robotics has been accepted by ICDCS 2023 Demo and Poster program. Congratulations to my supervised CUHKSZ undergraduate students Wenli Xiao (admitted by CMU Robotics Institute) and Tingwei Ye(now in NYU)!

[Apr. 2023] Our paper on incentivizing unbiased federated learning has been accepted to ICDCS 2023 (Track on AI for Distributed Systems and Distributed Systems for AI)

[Mar. 2023] Our paper "Optimization Design for Federated Learning in Heterogeneous 6G Networks" got accepted in IEEE Network, Special Issue on 6G Network AI Architecture for Customized Services and Applications, 2023.

[Sep. 2022] I joined Duke Kunshan University (DKU) as a Tenure-Track Assistant Professor.

[June, 2022] Prof. Jianwei Huang and I have organized a series of federated learning online seminars at AIRS in this June. The invited speakers and talk details are as follows:

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.

  • This initiative is led by Professor Bing Luo, with undergraduate students Mingxi Li, Yuxiang Lin, Ningyuan Yang, and Chen Shuhao.

  • In November, ChatDKU will be available to DKU community.
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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.
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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.
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Optimization and System Development for Federated Learning in Mobile and IoT Networks

  • 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.
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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.

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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.

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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.
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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)

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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)

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Resource Optimization for Distributed Cooperative Wireless Energy Harvesting Systems

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

Journal Papers

  • 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

(peer-reviewed, including workshops, posters and demos)

  • 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.
  • 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.
  • 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).

Services

Conference (Technical) Program Committee Member

  • AISTATS 2025

  • NeurIPS 2024

  • European Conference on Artifical Intelligence (ECAI) 2024

  • IEEE ICC 2020-2025

  • IEEE GLOBECOM 2020-2024

  • IEEE ICDCS 2023,2024

  • IEEE EDGE 2024 (senior PC)

  • IFIP NPC 2024

  • 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

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)