Bing Luo (罗冰)
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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
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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!!
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
News
[Aug. 2023] My Federated Learning research proposal (PI) received 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, who will become a PhD student at HKUST this Fall co-supervised by me and Prof. Xuanyu Cao!
[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:
Research Highlights
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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.
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I. Adaptive Client Sampling for Federated Learning
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II. Cost-Effective Federated Learning Design
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Game-Theoretic Incentive Mechanism for Unbiased Federated Learning
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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).
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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.
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Federated Reinforcement Learning for Robotics
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Resource Optimization for Distributed Coordinated Multi-Point (CoMP) Systems
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Resource Optimization for Distributed Cooperative Wireless Energy Harvesting Systems
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Publications (Note: Student co-authors (co-)supervised by me are underlined)
Journal Papers
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B. Luo, X. Ouyang, P. Sun, P. Han, N, Ding, J. Huang, “Optimization Design for Federated Learning in Heterogeneous 6G Networks,” accepted in IEEE Network, 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.
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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)
- 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
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B. Luo, J. Shao, Method and Apparatus for Online Parameter Selection in Minimizing the Total Cost of Federated Learning, CN202310485067.8, Apr. 2023, field
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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
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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
Graduate Course
Group
Principal Investigator (PI): Dr. Bing Luo
Ph.D. Students
- Jiaqi Shao (main supervised PhD student at HKUST, co-supervised with Prof. Xuanyu Cao)
- Xiang Li (co-supervised PhD student at CUHKSZ with Prof. Jianwei Huang)
- Langchen Liu (co-supervised PhD student at Yale with Prof. Lu Lu)
Master Students
- 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)
- Tianyi Zhang
Research Assistants
- Zijun Sun (UESTC)
- Mingxi Li (Wuhan University)
- Shengguang Cui (CUHKSZ)
- Zihan Li (CUHKSZ)
- Yutong Feng (CUHKSZ)
- Sichang He
- Beilong Tang
- Renzo Balcazar
- Aicha Slaitane
- Boyan Zhang
- Tianjun Yuan
Alumni (Selected from main 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 2020-2023
IEEE GLOBECOM 2020-2023
IEEE ICDCS 2023
NeurIPS - Federated Learning Workshop 2023
NeurIPS - Federated Learning Workshop 2022
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)
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IEEE Transactions on Cognitive Communications and Networking (TCCN)
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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)
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)
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