Bing Luo

Journal Article IEEE Transactions on Mobile Computing · January 1, 2026 In federated learning (FL), distributed users collaboratively train a neural network model under the coordination of a central server. However, time-varying client availability, coupled with non-independent and non-identically distributed (non-IID) dataset ... Full text Cite

Beyond Right to be Forgotten: Managing Heterogeneity Side Effects Through Strategic Incentives

Conference Mobihoc 2025 Proceedings of the 2025 International Symposium on Theory Algorithmic Foundations and Protocol Design for Mobile Networks and Mobile Computing · October 23, 2025 Federated Unlearning (FU) enables the removal of specific clients' data influence from trained models. However, in non-IID settings, removing clients creates critical side effects: remaining clients with similar data distributions suffer disproportionate p ... Full text Cite

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

Journal Article IEEE Transactions on Mobile Computing · January 1, 2025 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 non-independent and identical ... Full text Cite

Ten Challenging Problems in Federated Foundation Models

Journal Article IEEE Transactions on Knowledge and Data Engineering · January 1, 2025 Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of federated learning. This combination allows the large foundation models and the ... Full text Cite

FednP: Federated Unlearning With Multiple Client Set Partitions

Journal Article IEEE Transactions on Mobile Computing · January 1, 2025 Federated learning (FL) has garnered increased attention in the field of distributed machine learning and privacy computing. In the FL setup, effective and efficient unlearning algorithms are required to remove the impact of specific training data from the ... Full text Cite

Strategic Prompt Pricing for AIGC Services: A User-Centric Approach

Conference Proceedings of the International Symposium on Modeling and Optimization in Mobile Ad Hoc and Wireless Networks Wiopt · January 1, 2025 The rapid growth of AI-generated content (AIGC) services has created an urgent need for effective prompt pricing strategies, yet current approaches overlook users’ strategic two-step decision-making process in selecting and utilizing generative AI models. ... Full text Cite

SLEI3D: Simultaneous Exploration and Inspection via Heterogeneous Fleets under Limited Communication

Journal Article IEEE Transactions on Automation Science and Engineering · January 1, 2025 Robotic fleets such as unmanned aerial and ground vehicles have been widely used for routine inspections of static environments, where the areas of interest are known and planned in advance. However, in many applications, such areas of interest are unknown ... Full text Cite

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

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

Federated Unlearning with Multiple Client Partitions

Conference IEEE International Conference on Communications · January 1, 2024 Federated learning (FL) has recently received more and more attention in the joint field of distributed machine learning (ML) and privacy computing. Similar to the traditional ML systems, there exists the need of effective and efficient unlearning algorith ... Full text Cite

Fedkit: Enabling Cross-Platform Federated Learning for Android and iOS

Conference IEEE INFOCOM 2024 IEEE Conference on Computer Communications Workshops INFOCOM Wkshps 2024 · January 1, 2024 We present Fedkit, a federated learning (FL) system tailored for cross-platform FL research on Android and iOS devices. Fedkit pipelines cross-platform FL development by enabling model conversion, hardware-accelerated training, and cross-platform model agg ... Full text Cite

Tackling System-Induced Bias in Federated Learning: A Pricing-Based Incentive Mechanism

Conference Proceedings International Conference on Distributed Computing Systems · January 1, 2024 In federated learning (FL), distributed users collaboratively train a neural network model under the coordination of a central server. However, during the training process, clients often exhibit time-varying availability and have non-independent and non-id ... Full text Cite

Fed-MUnet: Multi-modal Federated Unet for Brain Tumor Segmentation

Conference 2024 IEEE International Conference on E Health Networking Application and Services Healthcom 2024 · January 1, 2024 Deep learning-based techniques have been widely utilized for brain tumor segmentation using both single and multi-modal Magnetic Resonance Imaging (MRI) images. Most current studies focus on centralized training due to the intrinsic challenge of data shari ... 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