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Diffusion Model Empowered Efficient Data Distillation Method for Cloud-Edge Collaboration

IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND networking 2025年第2期11卷 902-913页

作者： Chai, Ze Lin, Yijing Gao, Zhipeng Yu, Xinlei Xie, Zhiqiang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

The application of AI-generated models demands substantial amounts of data, which not only increases training time and memory consumption but also poses challenges to computation and communication loads in cloud-edge collaborative environments. Especially when edge devices have limited resources and network bandwidth is constrained, efficiently handling large-scale data in cloud-edge collaboration becomes a critical issue. Data distillation compresses large datasets into smaller, synthetic datasets, reducing transmission and computation overhead while maintaining model performance. However, existing data distillation methods face two main challenges: 1) improving accuracy without compromising distillation efficiency, and 2) the distilled dataset may retain backdoor attack triggers. To address these challenges, we propose an efficient data distillation method based on diffusion models, tailored for cloud-edge collaborative environments. We design a diffusion model training mechanism based on Kullback-Leibler (KL) divergence and contrastive loss to effectively distill synthetic datasets and enhance their accuracy, thereby reducing the data storage requirements on edge devices and alleviating communication burden with the cloud. Additionally, we introduce a dynamic channel weighting method based on an adaptive attention mechanism to retain essential features during the distillation process, improving model adaptability in cloud-edge collaborative environments. For the first time, We propose an optimization method based on diffusion model classification loss to realize backdoor attack in data set distillation in edge environment. Experimental results demonstrate that our method improves the accuracy of distilled datasets by 2.8% to 11.7% compared to traditional DNN data distillation methods and by 0.9% to 4.1% compared to existing diffusion model-based data distillation methods. Additionally, our attack methodology achieves backdoor attacks while maintaining an accuracy

关键词： Data models Collaboration Training Synthetic data Diffusion models Computational modeling Cloud computing Accuracy Image edge detection Load modeling Diffusion model data distillation backdoor attacks

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Delay- and Resource-Aware Satellite UPF Service Optimization

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第4期24卷 2564-2579页

作者： Wang, Chao Ma, Xiao Xing, Ruolin Li, Sisi Zhou, Ao Wang, Shangguang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Executing 5G core network functions on satellites has become crucial to enhance satellite network management and service capabilities. The User Plane Function (UPF) is responsible for efficient data traffic forwarding and is envisioned as a key and pioneering core network function that will be deployed on satellites. However, managing and providing services with satellite UPFs face dual challenges. Limited satellite resources constrain the user scale that a satellite UPF can service, resulting in an unguaranteed service delay. Moreover, the extremely rapid mobility of satellites renders it difficult for satellite UPFs to provide seamless services. To address the above challenges, this paper presents the first-of-its-kind service optimization scheme for satellite UPFs in terms of switch control, state migration, and traffic routing. To provide guaranteed service delay, we provide a theoretical analysis based on the M/G/1 queue model, demonstrating the service delay-resource consumption trade-off. A satellite UPF switch control scheme is integrated into the service optimization process, which can decrease satellite UPF service delay while saving satellite resources by adjusting the switch control parameters. To provide seamless services, we propose a satellite UPF-oriented state-aware service migration and traffic routing (UPF service optimization) algorithm. A policy network-based reinforcement learning approach is employed to dynamically perceive the satellite network's state as well as the satellite UPF switch state. Building upon the optimization of service delay through satellite UPF switch control, the processes of state-aware state migration and traffic routing are further employed to reduce delay, ensuring seamless service effectively. Experiments reveal that the proposed algorithm outperforms other benchmark algorithms under different metrics. The service delay is reduced by an average of 23.2% compared with other algorithms.

关键词： Satellites Switches Routing Optimization Delays Control systems Process control Low earth orbit satellites Base stations 5G mobile communication Satellite UPF service optimization delay-resource consumption trade-off policy network

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Part-Level Relationship Learning for Fine-Grained Few-Shot Image Classification

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IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 1448-1460页

作者： Wang, Chuanming Fu, Huiyuan Liu, Peiye Ma, Huadong Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Recently, an increasing number of few-shot image classification methods have been proposed, and they aim at seeking a learning paradigm to train a high-performance classification model with limited labeled samples. However, the neglect of part-level relationships causes few-shot methods to struggle to distinguish between closely similar subcategories, which makes it difficult for them to solve the fine-grained image classification problem. To tackle this challenging task, this paper proposes a fine-grained few-shot image classification method that exploits both intra-part and inter-part relationships among different samples. To establish comprehensive relationships, we first extract multiple discriminative descriptors from the input image, representing its different parts. Then, we propose to define the metric spaces by interpolating intra-part relationships, which can help the model adaptively find clear boundaries for these confusing classes. Finally, since the unlabeled image has high similarities to all classes, we project these similarities into a high-dimension space according to the inter-part relationship and interpolate a parameterized classifier to discover the subtle differences among these similar classes. To evaluate our proposed method, we conduct extensive experiments on various fine-grained datasets. Without any pre-train/fine-tuning process, our approach clearly outperforms previous few-shot learning methods, which demonstrates the effectiveness of our approach.

关键词： Measurement Feature extraction Image classification Extraterrestrial measurements Few shot learning Prototypes Neural networks Metalearning Image color analysis Hands Fine-grained image classification few-shot learning metric learning part-level relationship learning

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Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario

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SENSORS 2025年第1期25卷 269-269页

作者： Cao, Hui Han, Shujun Meng, Rui Xu, Xiaodong Zhang, Ping Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Satellite-ground communication is a critical component in the global communication system, significantly contributing to environmental monitoring, radio and television broadcasting, aerospace operations, and other domains. However, the technology encounters challenges in data transmission efficiency, due to the drastic alterations in the communication channel caused by the rapid movement of satellites. In comparison to traditional transmission methods, semantic communication (SemCom) technology enhances transmission efficiency by comprehending and leveraging the intrinsic meaning of information, making it ideal for image transmission in satellite communications. Nevertheless, current SemCom methods still struggle to adapt to varying channel conditions. To address this, we propose a SemCom transmission model based on a Channel Code-Book (CCB) for adaptive image transmission in diverse channel environments. Our model reconstructs and restores the original image by documenting fading and noise states under various channel conditions and dynamically adjusting the denoiser's model parameters. Extensive experimental results demonstrate that our CCB model outperforms three representative baseline models, including Deep JSCC, ASCN, and WITT in various environments and task conditions, achieving an advantage of more than 10 dB under high signal-to-noise ratio conditions.

关键词： semantic communication satellite-ground scenarios channel adaptive image transmission

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Growth-adaptive distillation compressed fusion model for network traffic identification based on IoT cloud-edge collaboration

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AD HOC NETWORKS 2025年 167卷

作者： Yang, Yang Fan, Chengwen Chen, Shaoyin Gao, Zhipeng Rui, Lanlan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

The development of the Internet of Things (IoT) has led to the rapid growth of the types and number of connected devices and has generated large amounts of complex and diverse traffic data. Traffic identification on edge servers solves the real-time and privacy requirements of IoT management and has attracted much attention, but still faces several problems: (1) traditional machine learning (ML) models rely on artificially constructed features, and the existing deep learning (DL) traffic identification models have reached their performance limit;and (2) insufficient computing resources of edge servers limit the possible improvement in the performance of deep learning models by increasing the number of parameters and structural complexity. To address these issues, we propose a lightweight fusion model. First, the Network-in-Network (NiN) model and Random Forest (RF) model are used on the cloud server to construct a traffic identification fusion model. The excellent representation extraction capability of the NiN compensates for the RF's dependence on manual feature extraction, and its modular structure is suitable for the subsequent model compression operations. Then, the NiN was distilled. We propose Growth-Adaptive Distillation to lightweight the NiN model, which can reduce the operation of manually adjusting the structure of the student model and ensure the efficiency and low power consumption of the fusion model deployment. In addition, both the RF in the cloud and the distilled NiN are deployed on the edge server. Comparisons with multiple algorithms on two network traffic datasets show that the proposed model achieves state-of-the-art performance while ensuring the use of minimal computational resources.

关键词： IoT Traffic identification Network-in-network Random forest Knowledge distillation Fusion model

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Explainable and Energy-Efficient Selective Ensemble Learning in Mobile Edge Computing Systems

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2025年第2期22卷 1744-1759页

作者： Feng, Lei Liao, Chaorui Shi, Yingji Zhou, Fanqin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Explainable ensemble learning combines explainable artificial intelligence (XAI) and ensemble learning (EL) to solve the closed-box problem of EL and provide a clear and transparent explanation of the decision-making process in the model. As a distributed machine learning architecture, EL deploys base learners trained with local data at edge node and infers on target tasks, then combines the inference results of the participating base learners. However, selecting all base learners into EL may result in wasting more computing resources and not obtain better performance. To address this issue, we put forward the definition of confidence level (ConfLevel) on the basis of XAI and verify its effectiveness as the metric of selecting the base learner. Then, we take the joint optimization model of considering high ConfLevel and low computing power to determine the participating base learners for selective ensemble learning (SEL). Due to the non-convex and combinatorial nature of the problem, we propose a node selection and power control algorithm on the premise of Benders' Decomposition (referred to BD-NSPC) to obtain the global optimal solution efficiently. In addition, simulation results show that BD-NSPC consumes about 30% less energy per EN on average and improves accuracy by 1-2% compared to other SEL algorithms. Besides, compared with federated learning (FL) framework, BD-NSPC reduces the energy consumption by about 25% and the latency by about 28%, achieving comparable accuracy in the edge computing system.

关键词： Computational modeling Machine learning algorithms Machine learning Ensemble learning Training Predictive models Explainable AI Prediction algorithms Energy consumption Accuracy Edge computing ensemble learning ensemble selection explainable artificial intelligence

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Quantum-Assisted Hierarchical Fuzzy Neural Network for Image Classification

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2025年第1期33卷 491-502页

作者： Wu, Shengyao Li, Runze Song, Yanqi Qin, Sujuan Wen, Qiaoyan Gao, Fei Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Deep learning is a powerful technique for data-driven learning in the era of Big Data. However, most deep learning models are deterministic models that ignore the uncertainty of data. Fuzzy neural networks are proposed to tackle this type of problem. In this article, we proposed a novel quantum assisted hierarchical fuzzy neural network (QA-HFNN). Different from classical fuzzy neural networks, QA-HFNN uses quantum neural networks (QNNs) to learn fuzzy membership functions. The model is a multifeature fusion learning algorithm with a parallel structural design that integrates quantum and classical neural networks. The classical network is used to capture high-dimensional neural features, the QNNs are designed to capture fuzzy logic features of the data, then, the two features are fused to form the final features to be classified. The experiment is performed on a classical computer, and the quantum circuit is built through a simulated quantum environment. The results indicate that the accuracy of QA-HFNN can equal to or even surpass classical methods in image classification tasks. The quantum circuit utilizes only a single qubit which is easy to implement. In addition, the fidelity of quantum circuit in a quantum noise environment is assessed, demonstrating that QA-HFNN has strong robustness. The time and computational complexity of QNNs was analyzed, further proving the effectiveness of the model.

关键词： Fuzzy logic Quantum computing Feature extraction Classification algorithms Image classification Task analysis Fuzzy neural networks Fuzzy member function fuzzy neural network image classification quantum neural network (QNN)

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Fast and Efficient Beam Alignment for Terahertz Communication via Sensing DoA of Leaky Waves From Intermediate Frequency Ports

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IEEE INTERNET OF THINGS JOURNAL 2025年第6期12卷 6196-6207页

作者： Yang, Chuang Zhang, Hongjie Wang, Yang Liu, Yuanwei Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Terahertz (THz) offers the availability of huge bandwidth to provide unprecedented data rate for the sixth-generation mobile communication and beyond. Since the narrow beam would be transmitted in the terminals to address severe path loss, THz beam alignment has been a source of significant overhead. The Direction of Arrival (DoA) sensed from a low-frequency band communication system could help fast beam alignment of THz communication. However, it is inefficient since it wastes hardware sources and requires exchange information between the sub-6 GHz and THz systems. In this article, based on the spatial similarity of the intermediate frequency (IF) channel and THz channel, a THz communication system is designed by sensing the DoA of the leaky waves from IF ports of the classical superheterodyne communication structure to aid the THz beam alignment, in which the IF part is controlled by a switching network. It is fast and efficient, since the DoA is employed and no additional communication baseband is used. Compared to the conventional beam alignment methods, the proposed shows well performance on accuracy, reactiveness and overhead, especially in scenarios for limited channel measurement as well as massive multiple-input multiple-output which are regular in THz communication.

关键词： Terahertz communications Switches OFDM Channel models Vectors Sensors Internet of Things Baseband Antennas Receivers Beam alignment Direction of Arrival (DoA) intermediate frequency (IF) leaky waves terahertz (THz) communication

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A congestion-aware user-cell association scheme to assist with traffic offloading in a three-tier heterogeneous network

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COMPUTER NETWORKS 2024年 245卷

作者： Han, Xiaoli Hu, Bo Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

In this work, we aim to expand the cell range in congestion areas. To achieve this goal, we integrate UAV network into macro-micro cellular network to enhance coverage. However, the gain of deploying heterogeneous nodes is limit if there are no CRE (cell range expanding) -based method to steer users towards low -power nodes. Therefore, in order to further improve system access capacity, we propose a congestionaware UA (user-cell association) scheme to offload queued traffic of cellular network into UAV network. To verify the superiority of proposed scheme, we utilize the low -complexity SG (stochastic -geometry) methods. Then in simulation analysis phase, we obtain five primary results: ( a ). The SG -based methods have lower computation complexity, and the convergence of derived expressions are verified. ( b ). The SG -based methods do not need the exact users' location but the users' density, which have improved the operability and robustness in infrastructure construction of wireless network. ( c ). The congestion -aware UA scheme can prompt network to adjust association tier flexibly, which is very suitable for traffic unevenly -distributed regions. ( d ). The optimal offsets of virtual power are estimated as B 2 = -4 dB for small -cell and B 3 = 3 dB for drone -cell, the resulting improvement in communication capacity is 8%. ( e ). We suggest the deployment density of UAV sites will no need to exceed 2 3 = 41 (pks/km 2 ).

关键词： Traffic offloading User-cell association scheme Integrated aerial-terrestrial network Stochastic geometry

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Enhancing QoE of Adaptive Video Streaming by Generating Fine-Grained Throughput

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO technology 2025年第4期35卷 3853-3866页

作者： An, Congkai Zhang, Huanhuan Kang, Jingyang Liu, Zhuo Zhou, Anfu Liu, Liang Ma, Huadong Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

On-demand video streaming continues to dominate the Internet, posing a formidable challenge in designing efficient adaptive bitrate (ABR) algorithms to enhance user quality-of-experience (QoE), particularly amplified by increasing video resolutions (e.g., from 1080P to 2K, 4K, and even 8K) and dynamic Internet conditions. Through a comprehensive study, we identify a common limitation in both existing throughput-based and hybrid-based ABR algorithms: they rely on coarse-grained network bandwidth estimation, missing detailed and accurate (i.e., millisecond-level) network variations. This often leads to misguided resolution (corresponding to bitrate level) decisions, resulting in unsatisfactory QoE. In this work, we propose SuperABR, a fine-grained throughput-driven ABR solution aimed at achieving the optimal bitrate adaptation. To accomplish this, SuperABR first incorporates a two-stage learning module, generating fine-grained future throughput to provide a near-Oracle network view. SuperABR then uses this fine-grained throughput to accurately calculate the download duration for a video chunk, transforming it into the optimal resolution decision via a custom-designed QoE benefit model. We have implemented SuperABR as a lightweight plug-in interface on a standard DASH framework and evaluate it over extensive real-world network traces. Extensive experiments demonstrate that SuperABR can generate accurate future throughput, resulting in a remarkable 1.21 similar to 1.46x QoE improvement over classic ABR solutions.

关键词： ABR algorithm fine-grained throughput QoE optimization

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