IEEE Transactions on Signal and Information Processing over Networks最新文献_第9页

Bipartite Graph Approximation by Eigenvalue Optimization 通过特征值优化实现双方图逼近

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-25 DOI: 10.1109/TSIPN.2024.3380351

Aimin Jiang;Xintong Shi;Yibin Tang;Yanping Zhu;Hon Keung Kwan

{"title":"Bipartite Graph Approximation by Eigenvalue Optimization","authors":"Aimin Jiang;Xintong Shi;Yibin Tang;Yanping Zhu;Hon Keung Kwan","doi":"10.1109/TSIPN.2024.3380351","DOIUrl":"10.1109/TSIPN.2024.3380351","url":null,"abstract":"Graphs are a powerful tool for representing entities and their relationships. Current advances in graph signal processing have made it possible to analyze graph-based data more effectively. Recent research show that, to ensure critical sampling, manyfilterbank design algorithms are only applicable to bipartite graphs. However, general graph signals may not exist on a bipartite graph structure. To overcome this difficulty, we propose in this paper a novel algorithm to find a bipartite approximation to the original non-bipartite graph while preserving its global structure. To achieve this goal, the original bipartite graph approximation (BGA) problem is constructed based on eigenvalue optimization of adjacency matrix, which is then relaxed so as to obtain a closed-form solution. We introduce the alternating direction method of multipliers (ADMM) to achieve a single bipartite graph or a set of edge-disjoint bipartite subgraphs that approximates the original graph. Additionally, we develop a distributed version of the BGA to address the computational challenges when processing large-scale graphs. Experimental results demonstrate the effectiveness of the proposed method and suggest it as a promising alternative approach for bipartite graph decomposition.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"307-319"},"PeriodicalIF":3.2,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140299077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scalable Distributed Optimization of Multi-Dimensional Functions Despite Byzantine Adversaries 尽管存在拜占庭对手，仍可对多维函数进行可扩展的分布式优化

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-22 DOI: 10.1109/TSIPN.2024.3379844

Kananart Kuwaranancharoen;Lei Xin;Shreyas Sundaram

{"title":"Scalable Distributed Optimization of Multi-Dimensional Functions Despite Byzantine Adversaries","authors":"Kananart Kuwaranancharoen;Lei Xin;Shreyas Sundaram","doi":"10.1109/TSIPN.2024.3379844","DOIUrl":"10.1109/TSIPN.2024.3379844","url":null,"abstract":"The problem of distributed optimization requires a group of networked agents to compute a parameter that minimizes the average of their local cost functions. While there are a variety of distributed optimization algorithms that can solve this problem, they are typically vulnerable to “Byzantine” agents that do not follow the algorithm. Recent attempts to address this issue focus on single dimensional functions, or assume certain statistical properties of the functions at the agents. In this paper, we provide two resilient, scalable, distributed optimization algorithms for multi-dimensional functions. Our schemes involve two filters, (1) a distance-based filter and (2) a min-max filter, which each remove neighborhood states that are extreme (defined precisely in our algorithms) at each iteration. We show that these algorithms can mitigate the impact of up to \u0000<inline-formula><tex-math>$F$</tex-math></inline-formula>\u0000 (unknown) Byzantine agents in the neighborhood of each regular agent. In particular, we show that if the network topology satisfies certain conditions, all of the regular agents' states are guaranteed to converge to a bounded region that contains the minimizer of the average of the regular agents' functions.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"360-375"},"PeriodicalIF":3.2,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140196592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph Receptive Transformer Encoder for Text Classification 用于文本分类的图形接收变换器编码器

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-21 DOI: 10.1109/TSIPN.2024.3380362

Arda Can Aras;Tuna Alikaşifoğlu;Aykut Koç

{"title":"Graph Receptive Transformer Encoder for Text Classification","authors":"Arda Can Aras;Tuna Alikaşifoğlu;Aykut Koç","doi":"10.1109/TSIPN.2024.3380362","DOIUrl":"10.1109/TSIPN.2024.3380362","url":null,"abstract":"By employing attention mechanisms, transformers have made great improvements in nearly all NLP tasks, including text classification. However, the context of the transformer's attention mechanism is limited to single sequences, and their fine-tuning stage can utilize only inductive learning. Focusing on broader contexts by representing texts as graphs, previous works have generalized transformer models to graph domains to employ attention mechanisms beyond single sequences. However, these approaches either require exhaustive pre-training stages, learn only transductively, or can learn inductively without utilizing pre-trained models. To address these problems simultaneously, we propose the Graph Receptive Transformer Encoder (GRTE), which combines graph neural networks (GNNs) with large-scale pre-trained models for text classification in both inductive and transductive fashions. By constructing heterogeneous and homogeneous graphs over given corpora and not requiring a pre-training stage, GRTE can utilize information from both large-scale pre-trained models and graph-structured relations. Our proposed method retrieves global and contextual information in documents and generates word embeddings as a by-product of inductive inference. We compared the proposed GRTE with a wide range of baseline models through comprehensive experiments. Compared to the state-of-the-art, we demonstrated that GRTE improves model performances and offers computational savings up to ˜100×.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"347-359"},"PeriodicalIF":3.2,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140205723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Partial Diffusion With Quantization Over Networks 部分扩散与网络量化

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-21 DOI: 10.1109/TSIPN.2024.3380374

Xiaoxian Lao;Chunguang Li

{"title":"Partial Diffusion With Quantization Over Networks","authors":"Xiaoxian Lao;Chunguang Li","doi":"10.1109/TSIPN.2024.3380374","DOIUrl":"10.1109/TSIPN.2024.3380374","url":null,"abstract":"Distributed estimation over networks has drawn much attention in recent years. In the problem of distributed estimation, a set of nodes is requested to estimate some parameter of interest from noisy measurements. The nodes interact with each other to carry out the task jointly. Many algorithms have been proposed for solving the distributed estimation problem, among which the diffusion strategy is well-accepted. Information diffusion among nodes consumes bandwidth and energy resources, while in real-world applications these resources are limited. To cope with the resources constraint, partial diffusion schemes are developed. Each node only disseminates a subset of entries of interested vector in each interaction. Besides the partial transmission, quantization is another widely adopted technique for saving the communication resources. The two methods work in different aspects and can be considered jointly to make the communication more efficient. In this paper, we propose a partial diffusion scheme with quantization. An optimization problem for communication resources allocation is formulated and solved. In each interaction, the nodes will adaptively determine whether to transmit more entries or assign more bits to quantize each entry. We derive sufficient conditions for convergence of the overall algorithm. We also demonstrate the advantages of the proposed scheme in terms of both convergence speed and estimation accuracy.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"320-331"},"PeriodicalIF":3.2,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140196492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition 通过状态分解实现分散优化的隐私保护推拉法

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-20 DOI: 10.1109/TSIPN.2024.3402430

Huqiang Cheng;Xiaofeng Liao;Huaqing Li;Qingguo Lü;You Zhao

{"title":"Privacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition","authors":"Huqiang Cheng;Xiaofeng Liao;Huaqing Li;Qingguo Lü;You Zhao","doi":"10.1109/TSIPN.2024.3402430","DOIUrl":"10.1109/TSIPN.2024.3402430","url":null,"abstract":"Distributed optimization is manifesting great potential in multiple fields, e.g., machine learning, control, resource allocation, etc. Existing decentralized optimization algorithms require sharing explicit state information among the agents, which raises the risk of private information leakage. To ensure privacy security, combining information security mechanisms, such as differential privacy and homomorphic encryption, with traditional decentralized optimization algorithms is a commonly used means. However, this may either sacrifice optimization accuracy or incur a heavy computational burden. To overcome these shortcomings, we develop a novel privacy-preserving decentralized optimization algorithm, named PPSD, that combines gradient tracking with a state decomposition mechanism. Specifically, each agent decomposes its state associated with the gradient into two substates. One substate is used for interaction with neighboring agents, and the other substate containing private information acts only on the first substate and thus is entirely agnostic to other agents. When the objective function is smooth and satisfies the Polyak-Łojasiewicz (PL) condition, PPSD attains an \u0000<inline-formula><tex-math>$R$</tex-math></inline-formula>\u0000-linear convergence rate. Moreover, the algorithm can preserve the normal agents' private information from being leaked to honest-but-curious attackers. Simulations further confirm the results.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"513-526"},"PeriodicalIF":3.2,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141151780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Consensus Analysis for Cooperative-Competitive Multiagent Systems Under False Data Injection Attacks via Dynamic Event-Triggered Observers 通过动态事件触发观测器对虚假数据注入攻击下的合作竞争型多代理系统进行共识分析

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-19 DOI: 10.1109/TSIPN.2024.3375611

Sangli Shi;Zhengxin Wang;Min Xiao;Guo-Ping Jiang;Jinde Cao

引用次数: 0

Piecewise-Constant Representation and Sampling of Bandlimited Signals on Graphs 带限信号在图上的片断恒定表示和采样

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-19 DOI: 10.1109/TSIPN.2024.3378122

Guangrui Yang;Qing Zhang;Lihua Yang

{"title":"Piecewise-Constant Representation and Sampling of Bandlimited Signals on Graphs","authors":"Guangrui Yang;Qing Zhang;Lihua Yang","doi":"10.1109/TSIPN.2024.3378122","DOIUrl":"10.1109/TSIPN.2024.3378122","url":null,"abstract":"Signal representations on graphs are at the heart of most graph signal processing techniques, allowing for targeted signal models for tasks such as denoising, compression, sampling, reconstruction and detection. This paper studies the piecewise-constant representation of bandlimited graph signals, thereby establishing the relationship between the bandlimited graph signal and the piecewise-constant graph signal. For this purpose, we first introduce the concept of \u0000<inline-formula><tex-math>$epsilon$</tex-math></inline-formula>\u0000-level piecewise-constant representation for a general signal space. Then, using a distance matrix, a single-layer piecewise-constant representation algorithm is proposed to find an \u0000<inline-formula><tex-math>$epsilon$</tex-math></inline-formula>\u0000-level piecewise-constant representation for bandlimited graph signals. On this basis, we further propose a multi-layer piecewise-constant representation algorithm, which can find a node partition with as few pieces as possible to represent bandlimited graph signals piecewise within a preset error bound. Finally, as an application, we apply the node partition obtained by the multi-layer algorithm to establish a sampling theory for bandlimited signals, which does not need to compute the eigendecomposition of a variation operator in both sampling and signal reconstruction. Numerical experiments show that the proposed algorithms have good performance.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"332-346"},"PeriodicalIF":3.2,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140168763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Gradient Tracking Protocol for Optimization Over Nabla Fractional Multi-Agent Systems 纳布拉分数多代理系统优化的梯度跟踪协议

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-17 DOI: 10.1109/TSIPN.2024.3402354

Shuaiyu Zhou;Yiheng Wei;Shu Liang;Jinde Cao

引用次数: 0

Remote State Estimation Under DoS Attacks in CPSs With Arbitrary Tree Topology: A Bayesian Stackelberg Game Approach 具有任意树状拓扑结构的 CPS 中 DoS 攻击下的远程状态估计：贝叶斯堆栈博弈方法

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-17 DOI: 10.1109/TSIPN.2024.3394776

Yuhan Wang;Wei Xing;Junfeng Zhang;Le Liu;Xudong Zhao

{"title":"Remote State Estimation Under DoS Attacks in CPSs With Arbitrary Tree Topology: A Bayesian Stackelberg Game Approach","authors":"Yuhan Wang;Wei Xing;Junfeng Zhang;Le Liu;Xudong Zhao","doi":"10.1109/TSIPN.2024.3394776","DOIUrl":"10.1109/TSIPN.2024.3394776","url":null,"abstract":"In this paper, we consider remote state estimation for an arbitrary tree topology in cyber-physical systems (CPSs) subject to Denial-of-Service (DoS) attacks. A sensor transmits its local estimation to the root node of the tree, and the root node transmits the optimal estimation to its child nodes until the leaf nodes are reached. In the meanwhile, a malicious attacker can jam all communication channels strategically connected to the attacked node. With the energy constraints in mind, both the defender and attacker adopt strategies that involve allocating energy to determine which nodes to protect or attack at each time step. A Bayesian Stackelberg game (BSG) framework with incomplete information is implemented, where the defender has no access to the available energy of the attacker exactly except for its probability distribution. In addition, a Markov decision process (MDP) and a Stackelberg Q-learning algorithm are presented to obtain the Stackelberg equilibrium (SE) policy over a finite time horizon. Finally, a numerical example is provided to demonstrate our main results.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"527-538"},"PeriodicalIF":3.2,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141061136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sensor-Fault Detection, Isolation and Accommodation for Natural-Gas Pipelines Under Transient Flow 瞬态流量下天然气管道的传感器故障检测、隔离和容纳

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-13 DOI: 10.1109/TSIPN.2024.3377134

Khadija Shaheen;Apoorva Chawla;Ferdinand Evert Uilhoorn;Pierluigi Salvo Rossi

{"title":"Sensor-Fault Detection, Isolation and Accommodation for Natural-Gas Pipelines Under Transient Flow","authors":"Khadija Shaheen;Apoorva Chawla;Ferdinand Evert Uilhoorn;Pierluigi Salvo Rossi","doi":"10.1109/TSIPN.2024.3377134","DOIUrl":"10.1109/TSIPN.2024.3377134","url":null,"abstract":"The monitoring of natural gas pipelines is highly dependent on the information provided by different types of sensors. However, sensors are prone to faults, which results in performance degradation and serious hazards such as leaks or explosions. To prevent catastrophic failures and ensure the safe and efficient operation of the pipelines, it is crucial to timely diagnose sensor faults in natural gas pipelines. This paper investigates model-based sensor fault diagnosis techniques in a natural-gas pipeline under transient flow. A fusing architecture based on distributed data fusion is used for implementing the sensor fault detection, isolation, and accommodation (SFDIA) mechanism. The fusing architecture consists of a set of local filters and an information mixer. The local filters estimate the state variables in parallel, which are subsequently transferred to the information mixer to evaluate the sensor faults and compute fault-free state estimates. In this paper, three different types of fusing filters, namely based on the ensemble Kalman filter (EnKF), fusing unscented Kalman filter (UKF), and fusing extended Kalman filter (EKF) are investigated for fault diagnosis. Results demonstrate that all three filters can successfully detect, isolate, and accommodate sensor faults.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"264-276"},"PeriodicalIF":3.2,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140125622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0