IEEE Transactions on Signal and Information Processing over Networks最新文献_第8页

Multi-Agent Bipartite Flocking Control Over Cooperation-Competition Networks With Asynchronous Communications 具有异步通信功能的合作-竞争网络上的多代理双方成群控制

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-04-03 DOI: 10.1109/TSIPN.2024.3384817

Zhuangzhuang Ma;Lei Shi;Kai Chen;Jinliang Shao;Yuhua Cheng

{"title":"Multi-Agent Bipartite Flocking Control Over Cooperation-Competition Networks With Asynchronous Communications","authors":"Zhuangzhuang Ma;Lei Shi;Kai Chen;Jinliang Shao;Yuhua Cheng","doi":"10.1109/TSIPN.2024.3384817","DOIUrl":"10.1109/TSIPN.2024.3384817","url":null,"abstract":"In this contribution, the bipartite flocking control problem of a set of autonomous mobile agents over cooperation-competition networks is investigated. Two kinds of asynchronous communication scenarios are considered, where each agent communicates with the neighbors only at certain time instants determined by its own clock, but not at other time instants. In addition, each agent adjusts the control input at all time instants in the first asynchronous scenario, and adjusts the control input only at its communication time instants in the second asynchronous scenario. Nonlinear positive and negative weight functions are designed to describe the effect of the distance between agents on the cooperation/competition degree in real interaction scenarios, where the farther (closer) the distance, the weaker (stronger) the cooperation/competition degree. With the help of signed graph theory and sub-stochastic matrix, the dynamic models under different asynchronous scenarios are analyzed, and the algebraic conditions for achieving bipartite flocking control are established separately. At last, the effectiveness of algebraic conditions is verified through numerical simulations.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"460-472"},"PeriodicalIF":3.2,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140590750","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

Distributed Event-Triggered Fault-Tolerant Consensus Control of Multi-Agent Systems Under DoS Attacks 多代理系统在 DoS 攻击下的分布式事件触发容错共识控制

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-04-03 DOI: 10.1109/TSIPN.2024.3384814

Chun Liu;Bin Jiang;Yang Li;Ron J. Patton

引用次数: 0

Composite Output Consensus Control for General Linear Multiagent Systems With Heterogeneous Mismatched Disturbances 具有异质不匹配干扰的一般线性多代理系统的复合输出共识控制

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-27 DOI: 10.1109/TSIPN.2024.3382427

Pan Yu;Yifan Ding;Kang-Zhi Liu;Xiaoli Li

{"title":"Composite Output Consensus Control for General Linear Multiagent Systems With Heterogeneous Mismatched Disturbances","authors":"Pan Yu;Yifan Ding;Kang-Zhi Liu;Xiaoli Li","doi":"10.1109/TSIPN.2024.3382427","DOIUrl":"10.1109/TSIPN.2024.3382427","url":null,"abstract":"This paper develops a composite output consensus control protocol for a general linear multiagent system subject to mismatched disturbances, which incorporates active disturbance-rejection control and fully distributed adaptive consensus control. To estimate and then cancel out the effect of mismatched disturbances on the outputs of the agents, heterogeneous generalized equivalent-input-disturbance estimators are constructed in the inner loop. Then a fully distributed adaptive feedback controller is designed to achieve consensus control based on the states of the designed heterogeneous observers for the agents. The restriction on the disturbances is lowered, the requirement for the global information of the communication topology is removed, and the exchanging information among agents is only relative estimated states. Further, the output consensus performance is analyzed for the closed-loop multiagent system. Our results complement and improve the results of the existing literature. Lastly, the effectiveness and superiority of the developed method are demonstrated through a numerical simulation and a comparison with the distributed extended-state-observer-based method.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"434-444"},"PeriodicalIF":3.2,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140313490","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

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