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Debin Liu - IEEE Xplore Author Profile
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Debin Liu

Publications
19
Citations
63
Publications by Year
20222025
Co-Authors:
Show All Co-Authors (37)
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Debin Liu

Affiliation

Huazhong University of Science and Technology, China
Xi’an Jiaotong University, China

Publication Topics

Biography

Debin Liu is currently working toward the Ph.D. degree with the School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China. His research interests include deep learning, edge computing, and Big Data.(Based on document published on 19 April 2023).
Publications
19
Citations
63
Publications by Year
20222025
Co-Authors:
Show All Co-Authors (37)
Author's Published Works

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WOM-FTL: An Efficient FTL for High-Density Flash Memory Through WOM-v Codes

Kai Tang;Jinhua Cui;Changhao Wen;Shiqiang Nie;Debin Liu;Yaliang Zhao;Laurence T. Yang

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Year: 2025 | Early Access Article |
High-density NAND flash memory, such as quadruple-level cell (QLC) flash, has has been widely adopted in emerging storage systems. However, its limited endurance and performance challenges necessitate novel solutions. Voltage-based write-once memory (WOM-v) codes have demonstrated their effectiveness in extending flash memory lifespan by reducing the erase count of flash blocks. Concurrently, secu...Show More

WOM-FTL: An Efficient FTL for High-Density Flash Memory Through WOM-v Codes

Kai Tang;Jinhua Cui;Changhao Wen;Shiqiang Nie;Debin Liu;Yaliang Zhao;Laurence T. Yang

Year: 2025 | Early Access Article |

Bayesian Nonnegative Tensor Completion With Automatic Rank Determination

Zecan Yang;Laurence T. Yang;Huaimin Wang;Honglu Zhao;Debin Liu

IEEE Transactions on Image Processing
Year: 2025 | Volume: 34 | Journal Article |
Nonnegative CANDECOMP/PARAFAC (CP) factorization of incomplete tensors is a powerful technique for finding meaningful and physically interpretable latent factor matrices to achieve nonnegative tensor completion. However, most existing nonnegative CP models rely on manually predefined tensor ranks, which introduces uncertainty and leads the models to overfit or underfit. Although the presence of CP...Show More

Bayesian Nonnegative Tensor Completion With Automatic Rank Determination

Zecan Yang;Laurence T. Yang;Huaimin Wang;Honglu Zhao;Debin Liu

Year: 2025 | Volume: 34 | Journal Article |

Panther: Practical Secure Two-Party Neural Network Inference

Jun Feng;Yefan Wu;Hong Sun;Shunli Zhang;Debin Liu

IEEE Transactions on Information Forensics and Security
Year: 2025 | Volume: 20 | Journal Article |
Cited by: Papers (4)
Secure two-party neural network (2P-NN) inference allows the server with a neural network model and the client with inputs to perform neural network inference without revealing their private data to each other. However, the state-of-the-art 2P-NN inference still suffers from large computation and communication overhead especially when used in ImageNet-scale deep neural networks. In this work, we d...Show More

Panther: Practical Secure Two-Party Neural Network Inference

Jun Feng;Yefan Wu;Hong Sun;Shunli Zhang;Debin Liu

Year: 2025 | Volume: 20 | Journal Article |

Tensor-Empowered Lightweight Representations for Personalized Federated Learning in Heterogeneous 6G Networks

Ruonan Zhao;Laurence T. Yang;Debin Liu;Xiangli Yang;Meiqi Wang

IEEE Network
Year: 2025 | Volume: 39, Issue: 2 | Magazine Article |
The Sixth Generation (6G) networks are designed to provide ubiquitous, customized and intelligent services for users. Federated Learning (FL) as a privacy-preserving Artificial Intelligence (AI) paradigm is expected to be a key enabler for realizing ubiquitous distributed AI in 6G networks. Given this, this paper mainly focuses on FL in 6G, combining the advantages of both FL and 6G to provide a n...Show More

Tensor-Empowered Lightweight Representations for Personalized Federated Learning in Heterogeneous 6G Networks

Ruonan Zhao;Laurence T. Yang;Debin Liu;Xiangli Yang;Meiqi Wang

Year: 2025 | Volume: 39, Issue: 2 | Magazine Article |

Lightweight Tensor-Enabled GRU for Trustworthy and Communication Efficient Federated Learning in Industrial IoT

Ruonan Zhao;Laurence T. Yang;Debin Liu;Wanli Lu;Xiangli Yang

IEEE Transactions on Industrial Informatics
Year: 2025 | Volume: 21, Issue: 3 | Journal Article |
Deep learning provides an intelligent analytical approach for Big Data analysis and feature extraction in Industrial Internet of Things (IIoT). However, due to concerns about data security and privacy disclosure, conventional data-centralized deep learning often faces difficulties about data famine and data islands. Federated learning (FL) as a novel privacy-preserving deep learning paradigm break...Show More

Lightweight Tensor-Enabled GRU for Trustworthy and Communication Efficient Federated Learning in Industrial IoT

Ruonan Zhao;Laurence T. Yang;Debin Liu;Wanli Lu;Xiangli Yang

Year: 2025 | Volume: 21, Issue: 3 | Journal Article |

Tensor-Empowered Hardware-Friendly Lightweight Deep Neural Networks for Vehicular Edge Computing

Debin Liu;Laurence T. Yang;Ruonan Zhao;Xiaokang Wang;Zhe Li;Honglu Zhao;Jinhua Cui

IEEE Transactions on Consumer Electronics
Year: 2024 | Early Access Article |
The high memory footprint, high computational overhead and high power consumption of deep neural networks are the main bottlenecks in deploying network models to vehicular edge devices. Furthermore, in the collaborative learning process, the large number of training parameters can cause high communication overhead between the vehicular edge devices and the cloud. The redundant floating-point opera...Show More

Tensor-Empowered Hardware-Friendly Lightweight Deep Neural Networks for Vehicular Edge Computing

Debin Liu;Laurence T. Yang;Ruonan Zhao;Xiaokang Wang;Zhe Li;Honglu Zhao;Jinhua Cui

Year: 2024 | Early Access Article |

Dual-Grained Lightweight Strategy

Debin Liu;Xiang Bai;Ruonan Zhao;Xianjun Deng;Laurence T. Yang

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2024 | Volume: 46, Issue: 12 | Journal Article |
Cited by: Papers (1)
Removing redundant parameters and computations before the model training has attracted a great interest as it can effectively reduce the storage space of the model, speed up the training and inference of the model, and save energy consumption during the running of the model. In addition, the simplification of deep neural network models can enable high-performance network models to be deployed to r...Show More

Dual-Grained Lightweight Strategy

Debin Liu;Xiang Bai;Ruonan Zhao;Xianjun Deng;Laurence T. Yang

Year: 2024 | Volume: 46, Issue: 12 | Journal Article |

TD3D: Tensor-based Discrete Diffusion Process for 3D Shape Generation

Jinglin Zhao;Debin Liu;Laurence T. Yang;Ruonan Zhao;Zheng Wang;Zhe Li

2024 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2024 | Conference Paper |
Based on the recent popularity of diffusion models, we have proposed a tensor-based diffusion model for 3D shape generation (TD3D). This generator is capable of tasks such as unconditional shape generation, shape completion and cross-modal shape generation. TD3D utilizes the Vector Quantized Variational Autoencoder (VQ-VAE) for encoding, compressing 3D shapes into compact latent representations, a...Show More

TD3D: Tensor-based Discrete Diffusion Process for 3D Shape Generation

Jinglin Zhao;Debin Liu;Laurence T. Yang;Ruonan Zhao;Zheng Wang;Zhe Li

Year: 2024 | Conference Paper |

TT-RNNPool3D: A Tensor-Based High-Order RNNPool Model for Mobile Edge Consumer Applications

Xueru Bai;Jieyuan Chen;Laurence T. Yang;Debin Liu;Xiaokang Wang;M. Jamal Deen

IEEE Transactions on Consumer Electronics
Year: 2024 | Early Access Article |
Cited by: Papers (4)
With the development of the new generation of information technologies such as the Internet-of-Things, Big Data, and High-Performance Computing, mobile edge devices are widely used in almost every aspect of our lives to provide personalized services. One key challenge how to efficiently process the multi-attribute data is essential to analyze the users’ information including the users’ consumption...Show More

TT-RNNPool3D: A Tensor-Based High-Order RNNPool Model for Mobile Edge Consumer Applications

Xueru Bai;Jieyuan Chen;Laurence T. Yang;Debin Liu;Xiaokang Wang;M. Jamal Deen

Year: 2024 | Early Access Article |

Multi-Tree Compact Hierarchical Tensor Recurrent Neural Networks for Intelligent Transportation System Edge Devices

Debin Liu;Laurence T. Yang;Ruonan Zhao;Xianjun Deng;Chenlu Zhu;Yiheng Ruan

IEEE Transactions on Intelligent Transportation Systems
Year: 2024 | Volume: 25, Issue: 8 | Journal Article |
Recurrent neural networks (RNNs) and their variants can efficiently capture the features of time-series characteristic data and are widely used for intelligent transportation tasks. Internet of Vehicles (IoV) edge devices deploying RNN models are an important impetus for the development of intelligent transportation system (ITS) and provide convenient services for users and managers. However, the ...Show More

Multi-Tree Compact Hierarchical Tensor Recurrent Neural Networks for Intelligent Transportation System Edge Devices

Debin Liu;Laurence T. Yang;Ruonan Zhao;Xianjun Deng;Chenlu Zhu;Yiheng Ruan

Year: 2024 | Volume: 25, Issue: 8 | Journal Article |

Sparse Bayesian Tensor Completion for Data Recovery in Intelligent IoT Systems

Honglu Zhao;Laurence T. Yang;Zecan Yang;Debin Liu;Xin Nie;Bocheng Ren

IEEE Internet of Things Journal
Year: 2024 | Volume: 11, Issue: 15 | Journal Article |
Cited by: Papers (2)
Intelligent Internet of Things (IoT), is an emerging paradigm that integrates lightweight intelligence algorithms to various IoT devices to provide convenient and intelligent services for modern life and production. For this purpose, data should be efficiently processed to explore the hidden information to elevate the intelligence of services. However, the IoT data are collected from a complex env...Show More

Sparse Bayesian Tensor Completion for Data Recovery in Intelligent IoT Systems

Honglu Zhao;Laurence T. Yang;Zecan Yang;Debin Liu;Xin Nie;Bocheng Ren

Year: 2024 | Volume: 11, Issue: 15 | Journal Article |

A Multi-Modal Tensor Ring Decomposition for Communication-Efficient and Trustworthy Federated Learning for ITS in COVID-19 Scenario

Ruonan Zhao;Laurence T. Yang;Debin Liu;Xiaokang Zhou;Xianjun Deng;Xueming Tang

IEEE Transactions on Intelligent Transportation Systems
Year: 2024 | Volume: 25, Issue: 5 | Journal Article |
Cited by: Papers (2)
Traffic and the movement of people are inextricably associated with the potential spread of COVID-19. In Intelligent Transportation System (ITS), Deep Learning (DL) traffic detection approaches driven by transportation big data have significant application values in monitoring, counting and classifying traffic vehicle information during the COVID-19 epidemic blockade, while DL COVID-19 medical dia...Show More

A Multi-Modal Tensor Ring Decomposition for Communication-Efficient and Trustworthy Federated Learning for ITS in COVID-19 Scenario

Ruonan Zhao;Laurence T. Yang;Debin Liu;Xiaokang Zhou;Xianjun Deng;Xueming Tang

Year: 2024 | Volume: 25, Issue: 5 | Journal Article |

Tensor-Based Secure Truthful Incentive Mechanism for Mobile Crowdsourcing in IoT-Enabled Maritime Transportation Systems

Ruonan Zhao;Laurence T. Yang;Debin Liu;Xianjun Deng;Xueming Tang;Sahil Garg

IEEE Transactions on Intelligent Transportation Systems
Year: 2024 | Volume: 25, Issue: 5 | Journal Article |
The evolution of the Internet of Things-enabled Maritime Transportation Systems (IoT-MTS) provides a sturdy data cornerstone for efficient maritime traffic scheduling and management. However, due to task heterogeneity and the limited computing power of individual vessels or stakeholders, although third-party clouds could provide powerful computing support for MTS, directly aggregating data from ve...Show More

Tensor-Based Secure Truthful Incentive Mechanism for Mobile Crowdsourcing in IoT-Enabled Maritime Transportation Systems

Ruonan Zhao;Laurence T. Yang;Debin Liu;Xianjun Deng;Xueming Tang;Sahil Garg

Year: 2024 | Volume: 25, Issue: 5 | Journal Article |

An Efficient Tensor-Based Transformer for Industrial Internet of Things

Debin Liu;Laurence T. Yang;Ruonan Zhao;Jinhua Cui;Xiangli Yang

IEEE Transactions on Network Science and Engineering
Year: 2024 | Volume: 11, Issue: 3 | Journal Article |
Transformer and its derivatives are widely used in industrial Internet of Things due to their excellent performance. However, the scale of these network models is exceptionally large, generating significant memory overhead and computational load during training and inference, as well as consuming large amounts of power resources. Therefore, these existing network models cannot be trained and deplo...Show More

An Efficient Tensor-Based Transformer for Industrial Internet of Things

Debin Liu;Laurence T. Yang;Ruonan Zhao;Jinhua Cui;Xiangli Yang

Year: 2024 | Volume: 11, Issue: 3 | Journal Article |

PointRCNN++: Towards More Accurate Two-Stage 3D Object Detection from Point Cloud

Debin Liu;Zengfu Wang

2023 China Automation Congress (CAC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
With significant potential in autonomous driving, and robotics, 3D object detection has garnered increasing attention among researchers. Leveraging its reliability in depth information, lidar stands out as one of the pivotal sensors in the realm of autonomous driving. PointRCNN, a point-based twostage object detector tailored for point cloud, has demonstrated commendable performance on many benchm...Show More

PointRCNN++: Towards More Accurate Two-Stage 3D Object Detection from Point Cloud

Debin Liu;Zengfu Wang

Year: 2023 | Conference Paper |

Tensor-Enabled Communication-Efficient and Trustworthy Federated Learning for Heterogeneous Intelligent Space–Air–Ground-Integrated IoT

Ruonan Zhao;Laurence T. Yang;Debin Liu;Wanli Lu

IEEE Internet of Things Journal
Year: 2023 | Volume: 10, Issue: 23 | Journal Article |
Cited by: Papers (7)
Federated learning (FL) could provide a promising privacy-preserving intelligent learning paradigm for space–air–ground-integrated Internet of Things (SAGI-IoT) by breaking down data islands and solving the dilemma between data privacy and data sharing. Currently, adaptivity, communication efficiency and model security are the three main challenges faced by FL, and they are rarely considered by ex...Show More

Tensor-Enabled Communication-Efficient and Trustworthy Federated Learning for Heterogeneous Intelligent Space–Air–Ground-Integrated IoT

Ruonan Zhao;Laurence T. Yang;Debin Liu;Wanli Lu

Year: 2023 | Volume: 10, Issue: 23 | Journal Article |

Tensor-Empowered Federated Learning for Cyber-Physical-Social Computing and Communication Systems

Laurence T. Yang;Ruonan Zhao;Debin Liu;Wanli Lu;Xianjun Deng

IEEE Communications Surveys & Tutorials
Year: 2023 | Volume: 25, Issue: 3 | Journal Article |
Cited by: Papers (13)
The deep fusion of human-centered Cyber-Physical-Social Systems (CPSSs) has attracted widespread attention worldwide and big data as the blood of CPSSs could lay a solid data cornerstone for providing more proactive and accurate wisdom services. However, due to concerns about data privacy and security, traditional data centralized learning paradigm is no longer suitable. Federated Learning (FL) as...Show More

Tensor-Empowered Federated Learning for Cyber-Physical-Social Computing and Communication Systems

Laurence T. Yang;Ruonan Zhao;Debin Liu;Wanli Lu;Xianjun Deng

Year: 2023 | Volume: 25, Issue: 3 | Journal Article |

Lightweight Tensor Deep Computation Model With Its Application in Intelligent Transportation Systems

Debin Liu;Laurence T. Yang;Ruonan Zhao;Jiawei Wang;Xia Xie

IEEE Transactions on Intelligent Transportation Systems
Year: 2022 | Volume: 23, Issue: 3 | Journal Article |
Cited by: Papers (8)
Deep computation models (DCMs) are widely used in intelligent transportation systems (ITS), like driving behavior detection, intelligent parking navigation and real-time road condition detection. Due to the multi-source heterogeneous nature of big data of the ITS, it is difficult for traditional DCMs to learn effective multi-modal data features. Although, the DCMs in tensor space can efficiently r...Show More

Lightweight Tensor Deep Computation Model With Its Application in Intelligent Transportation Systems

Debin Liu;Laurence T. Yang;Ruonan Zhao;Jiawei Wang;Xia Xie

Year: 2022 | Volume: 23, Issue: 3 | Journal Article |

A Tensor-Based Truthful Incentive Mechanism for Blockchain-Enabled Space-Air-Ground Integrated Vehicular Crowdsensing

Ruonan Zhao;Laurence T. Yang;Debin Liu;Xianjun Deng;Yijun Mo

IEEE Transactions on Intelligent Transportation Systems
Year: 2022 | Volume: 23, Issue: 3 | Journal Article |
Cited by: Papers (21)
Space-Air-Ground Integrated Network (SAGIN) as an efficient newly integration network could provide more comprehensive network services to meet the multifarious quality of service requirements in different Intelligent Transportation Systems (ITS). By taking advantage of SAGIN, Space-Air-Ground Integrated Vehicular Crowdsensing (SAGI-VCS) would have great potential and the services regarding ITS co...Show More

A Tensor-Based Truthful Incentive Mechanism for Blockchain-Enabled Space-Air-Ground Integrated Vehicular Crowdsensing

Ruonan Zhao;Laurence T. Yang;Debin Liu;Xianjun Deng;Yijun Mo

Year: 2022 | Volume: 23, Issue: 3 | Journal Article |

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