Bing Luo

Conference SenSys 2024 - Proceedings of the 2024 ACM Conference on Embedded Networked Sensor Systems · November 4, 2024 We present a privacy-preserving room occupancy estimation method using federated analytics of Bluetooth Low Energy (BLE) packets. By processing data locally and reporting only aggregated device counts, our approach preserves user privacy while achieving 95 ... Full text Cite

Demo: FedCampus: A Real-world Privacy-preserving Mobile Application for Smart Campus via Federated Learning & Analytics

Conference Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) · October 14, 2024 In this demo, we introduce FedCampus, a privacy-preserving mobile application for smart campus with federated learning (FL) and federated analytics (FA). FedCampus enables cross-platform on-device FL/FA for both iOS and Android, supporting continuously mod ... Full text Cite

Social Welfare Maximization for Federated Learning with Network Effects

Conference Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) · October 14, 2024 A proper mechanism design can help federated learning (FL) to achieve good social welfare by coordinating self-interested clients through the learning process. However, existing mechanisms neglect the network effects of client participation, leading to sub ... Full text Cite

Federated Spectrum Management Through Hedonic Coalition Formation

Conference ACM International Conference Proceeding Series · August 3, 2024 We present FedSM, a Federated Spectrum Management architecture to increase channel utilization (CU) and reduce latency, while protecting users' data privacy. We employ hedonic coalition formation game for spectrum allocation. Within each coalition, we desi ... 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 FL have ... 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 fundamental e ... Full text Cite

PriFairFed: A Local Differentially Private Federated Learning Algorithm for Client-Level Fairness

Journal Article IEEE Transactions on Mobile Computing · January 1, 2024 Local Differential Privacy (LDP) is a mechanism used to protect training privacy in Federated Learning (FL) systems, typically by introducing noise to data and local models. However, in real-world distributed edge systems, the nonindependent and identicall ... 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

Bing Luo

Selected Publications

Demo Abstract: Privacy-Preserving Room Occupancy Estimation Using Federated Analytics of BLE Packets

Demo: FedCampus: A Real-world Privacy-preserving Mobile Application for Smart Campus via Federated Learning & Analytics

Social Welfare Maximization for Federated Learning with Network Effects

Federated Spectrum Management Through Hedonic Coalition Formation

Adaptive Heterogeneous Client Sampling for Federated Learning Over Wireless Networks

Optimal Mechanism Design for Heterogeneous Client Sampling in Federated Learning

PriFairFed: A Local Differentially Private Federated Learning Algorithm for Client-Level Fairness

Optimization Design for Federated Learning in Heterogeneous 6G Networks

Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation

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

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

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

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

Cost-Effective Federated Learning in Mobile Edge Networks

Cost-Effective Federated Learning Design

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

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

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

Optimal co-phasing power allocation for coordinated OFDM transmission

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

Closed-form solution for minimizing power consumption in coordinated transmissions

Capacity analysis and optimal power allocation for coordinated transmission in MIMO-OFDM systems

Joint power allocation solutions for power consumption minimization in coordinated transmission system

Constant-Power Joint-Waterfilling for Coordinated Transmission

Optimal Joint Water-Filling for Coordinated Transmission over Frequency-Selective Fading Channels

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