Bing Luo (罗冰)

Temp 

Bing Luo

Assistant Professor of Data and Computational Science, Duke Kunshan University

Email: bing.luo [at] dukekunshan.edu.cn

Research Interests:

Federated learning and analytics, edge computing and IoT, optimization and game theory, 5G/6G wireless communications and energy harvesting systems

Recruiting

I have multiple positions for PhD, RAs, research scientists, and visiting interns.

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 bing.luo@dukekunshan.edu.cn.

Note: please make your email subject as [PhD/RA/research scientist/Intern Application] Name-School-Major.

Scholarship/Salary will be highly competitive!!

News

[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] We'll be presenting our FedCampus project at the Flower AI Summit 2024, one of the world's largest Federated Learning conference, in London, UK.

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

[Feb. 2024] We will be organizing 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. Stay tuned!

[Jan. 2024] Two FL papers got accepted in ICC 2024, one on "Client Sampling in Wireless Networks" collaborated with BUPT and Peng Cheng Lab; one on "Federated Unlearning" collaborated with Soochow University.

[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.

[Nov. 2023] I was elected as the Executive Committee Member at the Technical Committee of Federated Data and Federated Intelligence, China Association of Automation (CAA).

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

[Aug. 2023] My Federated Learning research proposal (PI) received 1,000,000 RMB funding from the 2023 Suzhou Basic Research Program (Frontier Technology Research). This project is in collaboration with China Mobile (Suzhou) R&D Center and Soochow University.

[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 Shao.

[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.

[Jan. 2023] I was elected as the Executive Committee at the Technical Committee of Computational Economics (TCCE), China Computer Federation (CCF)

[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:

Biography

I am a Tenure-Track Assistant Professor of Data and Computational Science at Duke Kunshan University (DKU), a partnership of Duke University and Wuhan University. Before joining DKU, I was a Postdoctoral Research Fellow at CUHK(SZ) and Yale University, advised by Prof. Jianwei Huang and Prof. Leandros Tassiulas. I received my Ph.D. from The University of Melbourne, advised by Assoc. Prof. Brian Krongold, B.E. and M.E. degrees from Beijing University of Posts and Telecommunications (BUPT), advised by Prof. Xiaofeng Tao.

I was a visiting scholar at Yale University, hosted by Prof. Leandros Tassiulas, Friedrich-Alexander-University Erlangen-Nuremberg, hosted by Prof. Robert Schober, Aalto University, hosted by Prof. Jyri Hämäläinen, and IBM T. J. Watson Research Center, hosted by Dr. Shiqiang Wang. Before pursuing my Ph.D. degree, I was a project manager at the Department of Networking, China Mobile Corporation, Beijing, China

Research Highlights

Temp 

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.

Temp 
Temp 

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'll be presenting our FedCampus project at the Flower AI Summit 2024, one of the world's largest Federated Learning conference, in London, UK.

Temp 

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.

Temp 
Temp 

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).

Temp 

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.

Temp 

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.

Temp Temp

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)

Temp 

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)

Temp 

Resource Optimization for Distributed Cooperative Wireless Energy Harvesting Systems

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

Journal Papers

  • 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)

  • 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.
  • J. Shao, S. Han, C. He, B. Luo, "Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data," in Workshop on Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities, in Conjunction with ICML 2023 (FL-ICML' 23), Jul. 2023.
  • 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.

Patents

  • B. Luo, J. Shao, Method and Apparatus for Online Parameter Selection in Minimizing the Total Cost of Federated Learning, CN202310485067.8, Apr. 2023, field

  • B. Luo, J. Shao, Method and Apparatus for Online Client Sampling in Minimizing the Training time of Federated Learning, CN 202310484383.3, Apr. 2023, field

  • B. Luo, Y. Feng, J. Huang, Method and Apparatus for Stackelberg Game based Incentive Mechanism for Unbiased Federated Learning, CN CN202310489754.7, Apr. 2023, field

  • B. Luo, J. Shao, J. Huang, Method and Apparatus for Frequent Items Mining Using Federated Analytics, CN202310365167.7, Mar. 2023, field

  • B. Luo, J. Shao, J. Huang, Method and Apparatus for Frequent Data Mining Based on Hierarchical Federated Analytics, CN202310330791.3, Mar. 2023, field

  • B. Luo, X. Li, J. Huang. Method and Apparatus for Dynamic Bandwidth Allocation in Heterogeneous Federated Learning, CN202211006603.3, August, 2022, field.

  • B. Luo, Z. Li, J. Huang. Method and Apparatus for Measuring Client Contribuation in Federated Learning, CN202210509693.1, May, 2022, field.

  • B. Luo, T. Ye, W. Xiao, J. Huang. Method and Apparatus for Model Aggregation for Federated Reinforcement Learning, CN202210320107.9, Mar. 2022, field.

  • B. Luo, J. Sun, B. Tan, W. Jin and C. He. Method and Apparatus for Distributed Power Control in LTE-A system, CN103906200A, Jul. 2014, granted.

  • Q. Cui, P. Zhang, X. Tao, B. Luo, A.A. Dowhuszko, and J. Hämäläinen. Method for allocating downlink transmission power of coordinated transmission devices in coordinated multi-point transmission system, U.S. Patent 8811147, Aug. 2014, granted.

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).

Group

Principal Investigator (PI): Dr. Bing Luo

Ph.D. Students

  • Jiaqi Shao (supervised PhD student at HKUST, co-supervised by Prof. Xuanyu Cao)
  • Mingxi Li (supervised PhD student at HKU, co-supervised by Prof. Yi Wang)
  • Zijun Sun (supervised PhD student at HKU, co-supervised by Prof. Xianhao Chen)
  • Xiang Li (co-supervised PhD student at CUHKSZ with Prof. Jianwei Huang)
  • Shengyuan Liang (co-supervised PhD student at BUPT with Prof. Qimei Cui)

Master Students

  • Qingning Zeng (Master student at DKU)
  • Tianyi Zhang (Master student at DKU, co-supervised with Prof. Lei Zhang)
  • Yan Gong (co-supervised Master student at Wuhan University with Prof. Dazhao Cheng)
  • Nanxi Wu (co-supervised Master student at Wuhan University with Prof. Chuang Hu)
  • Shijie Li (co-supervised Master student at BUPT with Prof. Qimei Cui)
  • Jiaxiang Geng (co-supervised Master student at BUPT with Prof. Yanzhao Hou)

Research Assistants

  • Yutong Feng (CUHKSZ)
  • Shengguang Cui (CUHKSZ)
  • Zihan Li (CUHKSZ)
  • Sichang He
  • Luyao Wang
  • Beilong Tang
  • Renzo Balcazar
  • Binyan Sun
  • Aicha Slaitane
  • Boyan Zhang
  • Tianjun Yuan
  • Shengyang Wang
  • Siyuan Cao
  • Long Qian
  • Zhenshan Zhang
  • Shu Pu
  • Ruisheng Sun
  • Xintong Zhang
  • Yuxiao Zhu

Alumni (Selected from supervised research students)

  • Wenli Xiao, research assistant, 2020-2022 → Master student at CMU Robotic Institute
  • Tingwei Ye, research assistant, 2020-2022 → Master student at NYU Tandon School of Engineering
  • Ziqi Li, research assistant, 2021-2022 → Master student at Oxford Mathematical Institute

Services

Conference (Technical) Program Committee Member

  • IEEE ICC 2020-2024

  • IEEE GLOBECOM 2020-2023

  • IEEE ICDCS 2023,2024

  • 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/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)

  • IEEE Internet of Things Journal (IoT-J)

Organizing Committee Member

Others

  • Executive Committee Member for Technical Committee of Federated Data and Intelligence, China Association of Automation (CAA). Nov. 2023 - Present

  • Executive Committee Member for Technical Committee of Computational Economics, China Computer Federation (CCF). Jan. 2023 – Present

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)

  • Outstanding Master Thesis, BUPT (2012)

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

  • National First Grade Scholarship (2011)

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