Scholars@Duke
Bing Luo

Bing Luo

Assistant Professor of Data and Computational Science at Duke Kunshan University
DKU Faculty
Manage Profile
Embed Profile Data

Selected Publications

Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks

Journal Article IEEE Transactions on Mobile Computing · January 1, 2024 Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communication bandwidth is limited. Recent works on the convergence analysis of F ... Full text Cite

Optimal Mechanism Design for Heterogeneous Client Sampling in Federated Learning

Journal Article IEEE Transactions on Mobile Computing · January 1, 2024 Federated learning (FL) provides a collaborative paradigm for distributedly training a global model while protecting clients' privacy. In addition to communication bottlenecks and non-i.i.d. data distributions, the FL framework introduces two fundam ... Full text Cite

Optimization Design for Federated Learning in Heterogeneous 6G Networks

Journal Article IEEE Network · March 1, 2023 With the rapid advancement of 5G networks, billions of smart Internet of Things (IoT) devices along with an enormous amount of data are generated at the network edge. While still at an early age, it is expected that the evolving 6G network will adopt advan ... Full text Cite

Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation

Conference Proceedings - International Conference on Distributed Computing Systems · January 1, 2023 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 incent ... Full text Cite

Poster: FedRos-Federated Reinforcement Learning for Networked Mobile-Robot Collaboration

Conference Proceedings - International Conference on Distributed Computing Systems · January 1, 2023 In this paper, we propose FedRos, a Federated Reinforcement Learning based multi-robot system, which enables networked robots collaboratively to train a shared model without sharing their private sensing data. Firstly, we present the FedRos pipeline that e ... Full text Cite

Distributed Multiantenna Frequency-Selective Energy Beamforming with Joint Total and Individual Power Constraints

Journal Article IEEE Transactions on Green Communications and Networking · December 1, 2022 We analyze distributed multi-antenna energy beamforming over frequency selective fading channels in wireless power transfer (WPT) systems with joint total and individual transmit power constraints. The constraints allow the WPT system to limit energy consu ... Full text Cite

Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling

Conference IEEE INFOCOM 2022 - IEEE Conference on Computer Communications · May 2, 2022 Full text Cite

Structural Properties of Optimal Power Allocation for DAS-OFDM under Joint Total and Individual Power Constraints

Journal Article IEEE Transactions on Green Communications and Networking · March 1, 2022 We study the structural properties of the optimal power allocation for a distributed antenna system with orthogonal frequency division multiplexing (DAS-OFDM), in which K remote radio heads (RRHs) allocate power over N > K subchannels under joint total and ... Full text Cite

Cost-Effective Federated Learning in Mobile Edge Networks

Journal Article IEEE Journal on Selected Areas in Communications · December 2021 Full text Cite

Cost-Effective Federated Learning Design

Conference IEEE INFOCOM 2021 - IEEE Conference on Computer Communications · May 10, 2021 Full text Cite

Optimal Power Allocation for DAS-OFDM under Joint Total and Individual Power Constraints

Conference 2019 IEEE Global Communications Conference (GLOBECOM) · December 2019 Full text Cite

Optimal Frequency-Selective Energy Beamforming with Joint Total and Individual Power Constraints

Conference 2019 IEEE Global Communications Conference (GLOBECOM) · December 2019 Full text Cite

Optimal Co-Phasing Power Allocation and Capacity of Coordinated OFDM Transmission with Total and Individual Power Constraints

Journal Article IEEE Transactions on Communications · October 1, 2019 This paper derives the optimal power allocation for a coordinated orthogonal frequency-division multiplexing (OFDM) transmission system in which $K$ coordinated transmission points (CTPs) coherently transmit and allocate power across $N$ subchannels under ... Full text Cite

Optimal co-phasing power allocation for coordinated OFDM transmission

Conference 2017 IEEE International Conference on Communications (ICC) · May 2017 Full text Cite

On the optimal power allocation for coordinated wireless backhaul in OFDM-based relay systems

Conference 2013 IEEE International Conference on Communications (ICC) · June 2013 Full text Cite

Closed-form solution for minimizing power consumption in coordinated transmissions

Journal Article EURASIP Journal on Wireless Communications and Networking · December 2012 Full text Cite

Constant-Power Joint-Waterfilling for Coordinated Transmission

Conference 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011 · December 2011 Full text Cite

Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks

Journal Article IEEE Transactions on Mobile Computing · January 1, 2024 Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communication bandwidth is limited. Recent works on the convergence analysis of F ... Full text Cite

Optimal Mechanism Design for Heterogeneous Client Sampling in Federated Learning

Journal Article IEEE Transactions on Mobile Computing · January 1, 2024 Federated learning (FL) provides a collaborative paradigm for distributedly training a global model while protecting clients' privacy. In addition to communication bottlenecks and non-i.i.d. data distributions, the FL framework introduces two fundam ... Full text Cite

Optimization Design for Federated Learning in Heterogeneous 6G Networks

Journal Article IEEE Network · March 1, 2023 With the rapid advancement of 5G networks, billions of smart Internet of Things (IoT) devices along with an enormous amount of data are generated at the network edge. While still at an early age, it is expected that the evolving 6G network will adopt advan ... Full text Cite

Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation

Conference Proceedings - International Conference on Distributed Computing Systems · January 1, 2023 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 incent ... Full text Cite

Poster: FedRos-Federated Reinforcement Learning for Networked Mobile-Robot Collaboration

Conference Proceedings - International Conference on Distributed Computing Systems · January 1, 2023 In this paper, we propose FedRos, a Federated Reinforcement Learning based multi-robot system, which enables networked robots collaboratively to train a shared model without sharing their private sensing data. Firstly, we present the FedRos pipeline that e ... Full text Cite

Distributed Multiantenna Frequency-Selective Energy Beamforming with Joint Total and Individual Power Constraints

Journal Article IEEE Transactions on Green Communications and Networking · December 1, 2022 We analyze distributed multi-antenna energy beamforming over frequency selective fading channels in wireless power transfer (WPT) systems with joint total and individual transmit power constraints. The constraints allow the WPT system to limit energy consu ... Full text Cite

Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling

Conference IEEE INFOCOM 2022 - IEEE Conference on Computer Communications · May 2, 2022 Full text Cite

Structural Properties of Optimal Power Allocation for DAS-OFDM under Joint Total and Individual Power Constraints

Journal Article IEEE Transactions on Green Communications and Networking · March 1, 2022 We study the structural properties of the optimal power allocation for a distributed antenna system with orthogonal frequency division multiplexing (DAS-OFDM), in which K remote radio heads (RRHs) allocate power over N > K subchannels under joint total and ... Full text Cite

Cost-Effective Federated Learning in Mobile Edge Networks

Journal Article IEEE Journal on Selected Areas in Communications · December 2021 Full text Cite

Cost-Effective Federated Learning Design

Conference IEEE INFOCOM 2021 - IEEE Conference on Computer Communications · May 10, 2021 Full text Cite

Optimal Power Allocation for DAS-OFDM under Joint Total and Individual Power Constraints

Conference 2019 IEEE Global Communications Conference (GLOBECOM) · December 2019 Full text Cite

Optimal Frequency-Selective Energy Beamforming with Joint Total and Individual Power Constraints

Conference 2019 IEEE Global Communications Conference (GLOBECOM) · December 2019 Full text Cite

Optimal Co-Phasing Power Allocation and Capacity of Coordinated OFDM Transmission with Total and Individual Power Constraints

Journal Article IEEE Transactions on Communications · October 1, 2019 This paper derives the optimal power allocation for a coordinated orthogonal frequency-division multiplexing (OFDM) transmission system in which $K$ coordinated transmission points (CTPs) coherently transmit and allocate power across $N$ subchannels under ... Full text Cite

Optimal co-phasing power allocation for coordinated OFDM transmission

Conference 2017 IEEE International Conference on Communications (ICC) · May 2017 Full text Cite

On the optimal power allocation for coordinated wireless backhaul in OFDM-based relay systems

Conference 2013 IEEE International Conference on Communications (ICC) · June 2013 Full text Cite

Closed-form solution for minimizing power consumption in coordinated transmissions

Journal Article EURASIP Journal on Wireless Communications and Networking · December 2012 Full text Cite

Constant-Power Joint-Waterfilling for Coordinated Transmission

Conference 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011 · December 2011 Full text Cite

Optimal Joint Water-Filling for OFDM Systems with Multiple Cooperative Power Sources

Conference 2010 IEEE Global Telecommunications Conference GLOBECOM 2010 · December 2010 Full text Cite