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Multivariate Time-Series Anomaly Detection Based on Dynamic Graph Neural Networks and Self-Distillation in Industrial Internet of Things

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IEEE Internet of Things Journal 2025年第9期12卷 12181-12192页

作者： Zhao, Mengmeng Peng, Haipeng Li, Lixiang Beijing University of Posts and Telecommunications Information Security Center State Key Laboratory of Networking and Switching Technology National Engineering Laboratory for Disaster Backup and Recovery Beijing100876 China Zaozhuang University Department of Information Science and Engineering Shandong 277160 China

Time-series anomaly detection is critical to securing the Industrial Internet of Things (IIoT). Although numerous deep learning-based methods have been proposed, these methods fail to consider the interdependencies between different dimensions of the data and often neglect the dynamic changes in these dependencies. Moreover, these methods utilize only the global features from the last layer of the network for anomaly detection. However, local features can capture subtle variations in the data, which are crucial for accurately detecting anomalies. To alleviate these problems, this article proposes a novel framework for detecting time-series anomalies, including four parts, namely, the graph structure learning module, the dynamic graph module, the anomaly scoring module, and the self-distillation. The graph structure learning module generates different graph structures based on the inputs, which will be used in the dynamic graph module. The dynamic graph module employs dynamic graph neural networks to capture the complex relationships within time series from both temporal and spatial dimensions. The anomaly scoring module obtains anomaly scores from predictions and observed values, and the model makes anomaly judgments based on these scores. Additionally, self-distillation enhances model performance by utilizing mutual learning between the teacher and student models, thereby integrating local and global information for better anomaly detection. We carry out a series of experiments on IIoT datasets, which verify the performance of the framework. The experimental results of the proposed method outperform other methods, demonstrating the advantage of our framework. © 2014 IEEE.

关键词： Time series analysis Anomaly detection Feature extraction Graph neural networks Industrial Internet of Things Training Data models Adaptation models Transformers Telecommunications

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IDA-NET: Individual Difference aware Medical Image Segmentation with Meta-Learning

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PATTERN RECOGNITION LETTERS 2025年 187卷 21-27页

作者： Zhang, Zheng Yin, Guanchun Ma, Zibo Tan, Yunpeng Zhang, Bo Zhuang, Yufeng Beijing Univ Posts & Telecommun Sch Modern Post Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Nat Pilot Software Engn Sch State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Individual differences in organ size and spatial distribution can lead to significant variations in the content of medical images at similar anatomical locations. These case-level differences are distinct from the domain shift between multi-source data, yet they can significantly affect model performance and are difficult to address using traditional transfer learning algorithms such as domain generalization. To address the individual difference issue, we propose an individual difference aware meta-learning strategy and introduce an individual discriminator module. These components are designed to learn features related to individual difference, enhancing the model's ability to accurately segment organs across different patients. Additionally, we present a Transformer-based U-Net framework that captures both long- and short-range dependencies from MR images. This framework utilizes a parallel attention module to address the limitations of self-attention and employs an inter-layer attention module to extract attention relationships across different layers. We evaluate our approach using the Synapse dataset. Results indicate that focusing on individual difference not only significantly improves the performance of various sub-modules, allowing our method to surpass several state-of-the-art methods, but also proves to be beneficial for many other methods as well.

关键词： Transformer U-Net Meta Learning Individual Difference Medical Image Segmentation

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SecureXGB: A Secure and Efficient Multi-party Protocol for Vertical Federated XGBoost

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Proceedings of the ACM on Management of Data 2025年第1期3卷 1-26页

作者： Zongda Han Xiang Cheng Wenhong Zhao Jiaxin Fu Zhaofeng He Sen Su State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

Extreme Gradient Boosting (XGBoost) demonstrates excellent performance in practice and is widely used in both industry and academic research. This extensive application has led to a growing interest in employing multi-party data to develop more robust XGBoost models. In response to increasing concerns about privacy leakage, secure vertical federated XGBoost is proposed. It employs secure multi-party computation techniques, such as secret sharing (SS), to allow multiple parties holding vertically partitioned data, i.e., disjoint features on the same samples, to collaborate in constructing an XGBoost model. However, the running efficiency is the primary obstacle to the practical application of existing protocols, especially in multi-party settings. The reason is that these protocols not only require the execution of data-oblivious computations to protect intermediate results, leading to high computational complexity, but also involve a large number of SS-based non-linear operations with high overheads, e.g., division operations in gain score calculation and comparison operations in best split selection. To this end, we present a secure and efficient multi-party protocol for vertical federated XGBoost, called SecureXGB, which can perform the collaborative training of an XGBoost model in an SS-friendly manner. In SecureXGB, we first propose a parallelizable multi-party permutation method, which can secretly and efficiently permute all samples before model training to reduce the reliance on data-oblivious computations. Then, we design a linear gain score that can be evaluated without involving division operations and has equivalent utility to the original gain score. Finally, we develop a synchronous best split selection method to secretly identify the best split with the maximum gain score using a minimal number of comparison operations. Experimental results demonstrate that SecureXGB can achieve better training efficiency than state-of-the-art protocols without the los

关键词： gradient boosting tree privacy-preserving machine learning vertical federated learning

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Electromagneticwave property inspired radio environment knowledge construction and artificial intelligence based verification for6G digital twin channel

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Frontiers of Information technology & Electronic Engineering 2025年第2期26卷 260-277页

作者： Jialin WANG Jianhua ZHANG Yutong SUN Yuxiang ZHANG Tao JIANG Liang XIA State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China China Mobile Research Institute Beijing 100053China

As the underlying foundation of a digital twin network(DTN),digital twin channel(DTC)can accurately depict the electromagnetic wave propagation in the air interface to support the DTN-based 6G wireless *** electromagnetic wave propagation is affected by the environment,constructing the relationship between the environment and radio wave propagation is the key to implementing *** the existing methods,the environmental information inputted into the neural network has many dimensions,and the correlation between the environment and the channel is unclear,resulting in a highly complex relationship construction *** solve this issue,we propose a unified construction method of radio environment knowledge(REK)inspired by the electromagnetic wave property to quantify the propagation contribution based on easily obtainable location *** effective scatterer determination scheme based on random geometry is proposed which reduces redundancy by 90%,87%,and 81%in scenarios with complete openness,impending blockage,and complete blockage,*** also conduct a path loss prediction task based on a lightweight convolutional neural network(CNN)employing a simple two-layer convolutional structure to validate REK’s *** results show that only 4 ms of testing time is needed with a prediction error of 0.3,effectively reducing the network complexity.

关键词： Digital twin channel Radio environment knowledge(REK)pool Wireless channel Environmental information Interpretable REK construction Artificial intelligence based knowledge verification

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Relay Node Placement with Base Stations in Wireless Sensor Networks Fault-Tolerant

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Chinese Journal of Electronics 2025年第4期23卷 794-800页

作者： Zhipeng Gao Kan Chen Xuesong Qiu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

It has been proposed to deploy relay nodes for the sake of prolonging Wireless sensor networks (WSN) lifetime, such that sensors transmit the sensed data to them which in turn delivers the data to base stations. For survivability requirements, relay placements which considers fault tolerant ability have been noticed and studied. While related works are limited or most existing works don't take factors such as fault tolerance, or base stations into account comprehensively in two tired WSN. We focus on fault tolerant relay node placement in two-tiered heterogeneous WSN with base stations. As far as we know, fault tolerance contains two fundamental aspects, for one is multi-coverage and the second is multi-path. It is a NP-hard problem and figure out an approximation, whose approximation ratio is enhanced to be $(18+\epsilon)$ . While a sub-problem approximation is also described as supplementary. Experimental results verify that the number of relay nodes deployed by our algorithm is somewhat superior to existed relay node placement solutions.

关键词： Measurement Fault tolerance Wireless sensor networks Base stations NP-hard problem Fault tolerant systems Approximation algorithms Sensors Relays

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Federated Low-Rank Adaptation for Large Models Fine-Tuning Over Wireless Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第1期24卷 659-675页

作者： Sun, Haofeng Tian, Hui Ni, Wanli Zheng, Jingheng Niyato, Dusit Zhang, Ping Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore 117583 Singapore

The emergence of large language models (LLMs) with multi-task generalization capabilities is expected to improve the performance of artificial intelligence (AI)-as-a-service provision in 6G networks. By fine-tuning LLMs, AI services can become more precise and tailored to the demands of different downstream tasks. However, centralized fine-tuning paradigms pose a potential risk to user privacy, and existing distributed fine-tuning methods incur significant wireless transmission burdens due to the large-scale parameter transmission of LLMs. To tackle these challenges, by leveraging the low rank feature in LLM fine-tuning, we propose a wireless over-the-air federated learning (AirFL) based low-rank adaptation (LoRA) framework that integrates LoRA and over-the-air computation (AirComp) to achieve efficient fine-tuning and aggregation. Based on multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM), we design a multi-stream AirComp scheme to fulfill the aggregation requirement of AirFL-LoRA. Furthermore, by deriving an optimality gap, we gain theoretical insights into the joint impact of rank selection and gradient aggregation distortion on the fine-tuning performance of AirFL-LoRA. Next, we formulate a non-convex problem to minimize the optimality gap, which is solved by the proposed backtracking-based alternating algorithm and the manifold optimization algorithm iteratively. Through fine-tuning LLMs for different downstream tasks, experimental results reveal that the AirFL-LoRA framework outperforms the state-of-the-art baselines on both training loss and perplexity, closely approximating the performance of FL with ideal aggregation.

关键词： Wireless networks Array signal processing OFDM Training LoRa Artificial intelligence Wireless sensor networks Performance evaluation Massive MIMO Tuning Low-rank adaption large language model federated learning over-the-air computation convergence analysis

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Efficient Multiple-Input-Multiple-Output Channel state Information Feedback: A Semantic-Knowledge-Base- Driven Approach

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ELECTRONICS 2025年第8期14卷 1666-1666页

作者： Tang, Ling Sun, Yaping Yao, Shumin Xu, Xiaodong Chen, Hao Luo, Zhiyong Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen 518107 Peoples R China Peng Cheng Lab Dept Broadband Commun Shenzhen 518066 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Massive multiple-input-multiple-output (MIMO) systems encounter substantial challenges in relation to channel state information (CSI) feedback;this is particularly the case in frequency division duplex systems, where the lack of channel reciprocity necessitates high-dimensional CSI transmission, resulting in substantial feedback overheads. Most existing deep learning methods have improved compression efficiency but still suffer from high feedback overheads due to their transmission of entire compressed vectors. To address this, we propose SKBNet, an innovative semantic knowledge base (SKB)-driven framework for efficient MIMO CSI feedback. By sharing the SKB between the transmitter and receiver, SKBNet transmits only index values, significantly reducing feedback overheads while achieving efficient CSI compression and accurate reconstruction. The simulation results demonstrate that SKBNet outperforms many existing methods in normalized mean square error, cosine similarity, and feedback bit-count at various compression ratios. This framework offers a promising solution for low-overhead, high-precision CSI feedback for future semantic communication networks.

关键词： semantic communication MIMO CSI feedback semantic knowledge base

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A survey of Machine Learning-based Physical-Layer Authentication in wireless communications

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2025年 235卷

作者： Meng, Rui Xu, Bingxuan Xu, Xiaodong Sun, Mengying Wang, Bizhu Han, Shujun Lv, Suyu Zhang, Ping Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Peng Cheng Lab Dept Broadband Commun Shenzhen 518066 Guangdong Peoples R China Beijing Univ Technol Sch Informat Sci & Technol Beijing 100124 Peoples R China

To ensure secure and reliable communication in wireless systems, authenticating the identities of numerous nodes is imperative. Traditional cryptography-based authentication methods suffer from issues such as low compatibility, reliability, and high complexity. Physical-Layer Authentication (PLA) is emerging as a promising complement due to its exploitation of unique properties in wireless environments. Recently, Machine Learning (ML)-based PLA has gained attention for its intelligence, adaptability, universality, and scalability compared to non-ML approaches. However, a comprehensive overview of state-of-the-art ML-based PLA and its foundational aspects is lacking. This paper presents a comprehensive survey of characteristics and technologies that can be used in the ML-based PLA. We categorize existing ML-based PLA schemes into two main types: multi-device identification and attack detection schemes. In deep learning-based multi-device identification schemes, Deep Neural Networks are employed to train models, avoiding complex processing and expert feature transformation. Deep learning-based multi-device identification schemes are further subdivided, with schemes based on Convolutional Neural Networks being extensively researched. In ML-based attack detection schemes, receivers utilize intelligent ML techniques to set detection thresholds automatically, eliminating the need for manual calculation or knowledge of channel models. ML-based attack detection schemes are categorized into three sub-types: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Additionally, we summarize open-source datasets used for PLA, encompassing Radio Frequency fingerprints and channel fingerprints. Finally, this paper outlines future research directions to guide researchers in related fields.

关键词： Physical-layer authentication Machine learning Identity security

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Detection and Multiparameter Estimation for NLoS Targets: An RIS-Assisted Framework

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2025年 73卷 1470-1484页

作者： Yu, Zhouyuan Hu, Xiaoling Liu, Chenxi Tao, Qin Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou 311121 Peoples R China

Reconfigurable intelligent surface (RIS) has the potential to enhance sensing performance, due to its capability of reshaping the echo signals. Different from the existing literature, which has commonly focused on RIS beamforming optimization, in this paper, we pay special attention to designing effective signal processing approaches to extract sensing information from RIS-reshaped echo signals. To this end, we investigate an RIS-assisted non-line-of-sight (NLoS) target detection and multi-parameter estimation problem in orthogonal frequency division multiplexing (OFDM) systems. To address this problem, we first propose a novel detection and direction estimation framework, including a low-overhead hierarchical codebook that allows the RIS to generate three-dimensional beams with adjustable beam direction and width, a delay spectrum peak-based beam training scheme for detection and direction estimation, and a beam refinement scheme for further enhancing the accuracy of the direction estimation. Then, we propose a target range and velocity estimation scheme by extracting the delay-Doppler information from the RIS-reshaped echo signals. Numerical results demonstrate that the proposed schemes can achieve a 99.7\% target detection rate, a 10(-3)-rad level direction estimation accuracy, and a 10(-6)-m/10(-5)-m/s level range/velocity estimation accuracy.

关键词： Sensors Estimation Array signal processing OFDM Training Object detection Accuracy Reconfigurable intelligent surfaces Data mining Location awareness Reconfigurable intelligent surface target detection direction estimation range and velocity estimation beam training

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FLCSDet: Federated Learning-Driven Cross-Spatial Vessel Detection for Maritime Surveillance With Privacy Preservation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第1期26卷 1177-1192页

作者： Huang, Yanhong Liu, Wen Lin, Yijing Kang, Jiawen Zhu, Fenghua Wang, Fei-Yue Wuhan Univ Technol Hubei Key Lab Inland Shipping Technol Sch Nav Wuhan 430063 Peoples R China State Key Lab Maritime Technol & Safety Wuhan 430063 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China

Maritime surveillance plays a vital role in reducing maritime accidents and improving maritime safety. To enhance situational awareness for maritime movements, deep learning-based visual object detection has become an important part of maritime surveillance. However, the detection results are highly dependent on the training datasets collected from different departments (i.e., clients). If the sub-datasets from departments are sensitive and private in cross-department maritime surveillance, it will be intractable to directly combine these sub-datasets to train the learning-based object detection method. To solve this issue, we propose a federated learning-driven cross-spatial vessel detection model, called FLCSDet, for maritime surveillance with privacy preservation. In particular, an efficient multi-scale attention module is integrated into our FLCSDet to achieve local cross-spatial feature learning. To improve the federated-learning aggregation method, we propose an optimized algorithm based on the proportion of valid data on departments to adaptively select the allocating weights and preserve the specific characteristics of client data. In addition, we employ transfer learning to further improve the robustness and convergence of our FLCSDet under different experimental scenarios. Compared with several representative federated learning-based detection methods, our FLCSDet could achieve superior detection performance in terms of both quantitative and qualitative results. Moreover, comprehensive experiments conducted on real datasets from both inland waterways and open seas demonstrate the robustness and generalization of our method in intelligent transportation systems. The source code is available at https://***/huangyanh/FLCSDet.

关键词： Surveillance Data privacy Data models Visualization Training Adaptation models Safety Accuracy Traffic control Federated learning Maritime surveillance vessel detection federated learning privacy preservation

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