IEEE Transactions on Signal and Information Processing over Networks最新文献

Learning Optimal Graph Filters for Clustering of Attributed Graphs 学习属性图聚类的最优图过滤器

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-06-02 DOI: 10.1109/TSIPN.2025.3574855

Meiby Ortiz-Bouza;Selin Aviyente

{"title":"Learning Optimal Graph Filters for Clustering of Attributed Graphs","authors":"Meiby Ortiz-Bouza;Selin Aviyente","doi":"10.1109/TSIPN.2025.3574855","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3574855","url":null,"abstract":"Many real-world systems can be represented as graphs where the different entities in the system are presented by nodes and their interactions by edges. An important task in studying large datasets with graphical structure is graph clustering. While there has been a lot of work on graph clustering using the connectivity between the nodes, many real-world networks also have node attributes. Clustering attributed graphs requires joint modeling of graph structure and node attributes. Recent work has focused on combining these two complementary sources of information through graph convolutional networks and graph filtering. However, these methods are mostly limited to lowpass filtering and do not explicitly learn the filter parameters for the clustering task. In this paper, we introduce a graph signal processing based approach, where we learn the parameters of Finite Impulse Response (FIR) and Autoregressive Moving Average (ARMA) graph filters optimized for clustering. The proposed approach is formulated as a two-step iterative optimization problem, focusing on learning interpretable graph filters that are optimal for the given data and that maximize the separation between different clusters. The proposed approach is evaluated on attributed networks and compared to the state-of-the-art methods.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"520-534"},"PeriodicalIF":3.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11021005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144264270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-Protection Method Against Eavesdroppers for Distributed State Estimation 分布式状态估计防窃听双保护方法

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-04-23 DOI: 10.1109/TSIPN.2025.3563774

Yan Yu;Chao Yang;Wenjie Ding;Wen Yang;Xiaofan Wang

引用次数: 0

Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing 在线联邦学习中的弹性：通过部分共享减轻模型中毒攻击

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-04-17 DOI: 10.1109/TSIPN.2025.3559444

Ehsan Lari;Reza Arablouei;Vinay Chakravarthi Gogineni;Stefan Werner

{"title":"Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing","authors":"Ehsan Lari;Reza Arablouei;Vinay Chakravarthi Gogineni;Stefan Werner","doi":"10.1109/TSIPN.2025.3559444","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3559444","url":null,"abstract":"Federated learning (FL) allows training machine learning models on distributed data without compromising privacy. However, FL is vulnerable to model-poisoning attacks where malicious clients tamper with their local models to manipulate the global model. In this work, we investigate the resilience of the partial-sharing online FL (PSO-Fed) algorithm against such attacks. PSO-Fed reduces communication overhead by allowing clients to share only a fraction of their model updates with the server. We demonstrate that this partial sharing mechanism has the added advantage of enhancing PSO-Fed's robustness to model-poisoning attacks. Through theoretical analysis, we show that PSO-Fed maintains convergence even under Byzantine attacks, where malicious clients inject noise into their updates. Furthermore, we derive a formula for PSO-Fed's mean square error, considering factors like stepsize, attack probability, and the number of malicious clients. Interestingly, we find a non-trivial optimal stepsize that maximizes PSO-Fed's resistance to these attacks. Extensive numerical experiments confirm our theoretical findings and showcase PSO-Fed's superior performance against model-poisoning attacks compared to other leading FL algorithms.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"388-400"},"PeriodicalIF":3.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883475","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 Generic Framework for Fixed-Time Synchronization of Large Delayed Impulsive Neural Networks 大延迟脉冲神经网络固定时间同步的通用框架

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-04-16 DOI: 10.1109/TSIPN.2025.3561551

Yishu Wang;Jianquan Lu;Xinsong Yang;Bangxin Jiang

{"title":"A Generic Framework for Fixed-Time Synchronization of Large Delayed Impulsive Neural Networks","authors":"Yishu Wang;Jianquan Lu;Xinsong Yang;Bangxin Jiang","doi":"10.1109/TSIPN.2025.3561551","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3561551","url":null,"abstract":"This article investigates fixed-time synchronization (FxTS) for neural networks with delayed impulses. There are two main challenges associated with the delay in this area. One is that it causes the networks to oscillate close to the equilibrium. The other is that it causes the synchronization criterion to be very conservative, particularly when the impulses contain large (exceeding impulsive intervals) delays. To overcome these challenges, we propose the concept of equivalent impulsive sequence and the method of delayed impulsive sequence decomposition, respectively. Meanwhile, we establish a framework that equivalently transforms the networks with large delays into a collection of networks with small (shorter than impulsive intervals) delays. Particularly, this transformation is a sufficient and necessary condition, and thus does not induce any conservatism. Following this, we delineate the FxTS criteria for the networks containing synchronizing and desynchronizing impulses. Interestingly, it is shown that under some conditions, the large delay has no effect on the FxTS criterion of networks with synchronizing impulses, but destroys those of networks with desynchronizing impulses. Furthermore, we prove the necessity and rationality of the FxTS criterion. Finally, the conclusions are substantiated through numerical demonstrations.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"439-449"},"PeriodicalIF":3.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913409","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

Dual Averaging for Distributed Unbalanced Optimization With Delayed Information 延迟信息分布式不平衡优化的双重平均法

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-04-11 DOI: 10.1109/TSIPN.2025.3559433

Qing Huang;Yuan Fan;Songsong Cheng

{"title":"Dual Averaging for Distributed Unbalanced Optimization With Delayed Information","authors":"Qing Huang;Yuan Fan;Songsong Cheng","doi":"10.1109/TSIPN.2025.3559433","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3559433","url":null,"abstract":"In this paper, we study a category of distributed constrained optimization problems where each agent has access to local information, communicates with its neighbors, and cooperatively minimizes the aggregated cost functions over time-varying unbalanced graphs. To address the considered problems, we propose a distributed dual averaging algorithm based on a row-stochastic weighted matrix (DDAR), which improves the robustness of network topology compared to conventional push-sum algorithms. Moreover, we develop a modified version of DDAR with delayed information (DDARD), which considers the delays of both network communication and gradient calculation, enhancing the algorithm's flexibility in communication and iteration. Our analysis demonstrates that the DDAR and DDARD achieve the optimal value at rates of <inline-formula><tex-math>${mathcal {O}}(frac{N}{(1-lambda)sqrt{T}})$</tex-math></inline-formula> and <inline-formula><tex-math>${mathcal {O}}(frac{{tilde{tau }}_{}^{2}N}{(1-{tilde{lambda }})sqrt{T}})$</tex-math></inline-formula>, respectively. Finally, the theoretical results are confirmed by simulation on a logistic regression problem.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"366-377"},"PeriodicalIF":3.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875169","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 Proportional-Integral Algorithms for Multiple Coalition Games Under Limited Communication Resources 有限通信资源下多联盟博弈的分布式比例积分算法

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-04-10 DOI: 10.1109/TSIPN.2025.3559442

Jiaxun Liu;Dong Wang;Jiashuo Liu;Xiwang Dong

{"title":"Distributed Proportional-Integral Algorithms for Multiple Coalition Games Under Limited Communication Resources","authors":"Jiaxun Liu;Dong Wang;Jiashuo Liu;Xiwang Dong","doi":"10.1109/TSIPN.2025.3559442","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3559442","url":null,"abstract":"This paper studies the algorithm design for multiple coalition games under limited communication resources, in which the players in the same coalition cooperatively optimize the summation of cost functions in this coalition and do not care about the costs of other coalitions. To address this game, we develop a distributed proportional-integral algorithm based on the coalition estimate strategy and the proportional-integral principle. Furthermore, when the communication resource is concretely quantified by bit rates in communication channels, we propose a coding-decoding-based distributed proportional-integral algorithm based on the distributed proportional-integral algorithm and coding-decoding rules for seeking the Nash equilibrium of multiple coalition games. It proves that both algorithms linearly and precisely converge to the Nash equilibrium in spite of limited communication resources. Then, the necessary and sufficient condition for the linear convergence of the proposed algorithm about the requirement of bit rates is presented. Moreover, the relationship between the bit rate and the convergence speed of the proposed algorithm is also theoretically explained. Lastly, the simulation in formation problems of unmanned vehicle swarms is presented to demonstrate the effectiveness of proposed algorithms.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"450-459"},"PeriodicalIF":3.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090763","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

The Coherence and Robustness Analysis for a Family of Unbalanced Networks 一类不平衡网络的相干性和鲁棒性分析

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-04-09 DOI: 10.1109/TSIPN.2025.3555164

Jia-Bao Liu;Xu Wang;Liang Hua;Jinde Cao;Liping Chen

{"title":"The Coherence and Robustness Analysis for a Family of Unbalanced Networks","authors":"Jia-Bao Liu;Xu Wang;Liang Hua;Jinde Cao;Liping Chen","doi":"10.1109/TSIPN.2025.3555164","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3555164","url":null,"abstract":"This study focuses on the coherence in a class of unbalanced networks, emphasizing the investigation of the impact of leader selection and network parameters on coherence. We discuss three different categories of consensus algorithms: leaderless algorithms of first and second orders, and first-order leader-follower algorithm. Firstly, we derive exact solutions for leaderless network coherence in the first and second orders. Secondly, we design various leader allocation schemes for the first-order leader-follower networks, obtaining expressions correlated with the network scale <inline-formula><tex-math>$N$</tex-math></inline-formula>, the double-ring cardinalities <inline-formula><tex-math>$p$</tex-math></inline-formula> and <inline-formula><tex-math>$q$</tex-math></inline-formula>, and the number of leaders <inline-formula><tex-math>$k$</tex-math></inline-formula>. Thirdly, we conduct numerical simulations and robustness analyses to explore the impact of leader quantity and topology structure on network coherence. Specifically, the conclusions are as follows: (1) When <inline-formula><tex-math>$N$</tex-math></inline-formula> decreases, an increase in <inline-formula><tex-math>$k$</tex-math></inline-formula> demonstrates improved network coherence. (2) The smaller the difference between <inline-formula><tex-math>$p$</tex-math></inline-formula> and <inline-formula><tex-math>$q$</tex-math></inline-formula>, the better the coherence. (3) The smaller the sum of <inline-formula><tex-math>$p$</tex-math></inline-formula> and <inline-formula><tex-math>$q$</tex-math></inline-formula>, the stronger the robustness. (4) Uniform distribution of <inline-formula><tex-math>$k_{1}$</tex-math></inline-formula> and <inline-formula><tex-math>$k_{2}$</tex-math></inline-formula> leads to optimal coherence. Finally, the Laplacian energy and Kirchhoff index of the network are analyzed.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"378-387"},"PeriodicalIF":3.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883261","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

Multi-Rate Sampled-Data Secure Fusion Estimation Against Malicious Hybrid Attacks 针对恶意混合攻击的多速率采样数据安全融合估计

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-04-09 DOI: 10.1109/TSIPN.2025.3559434

Haiyu Song;Siqing Ye;Peng Shi;Wen-An Zhang;Li Yu

引用次数: 0

Distributed Training of Large Graph Neural Networks With Variable Communication Rates 变通信速率大图神经网络的分布式训练

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-04-03 DOI: 10.1109/TSIPN.2025.3557584

Juan Cerviño;Md Asadullah Turja;Hesham Mostafa;Nageen Himayat;Alejandro Ribeiro

引用次数: 0

Distributed Optimization of Heterogeneous Linear Multi-Agent Systems With Unknown Disturbances and Optimal Gain Tuning 具有未知扰动的异构线性多智能体系统的分布优化与最优增益调谐

IF 3 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-03-29 DOI: 10.1109/TSIPN.2025.3574852

Mengmeng Duan;Shanying Zhu;Ziwen Yang;Cailian Chen;Xinping Guan

{"title":"Distributed Optimization of Heterogeneous Linear Multi-Agent Systems With Unknown Disturbances and Optimal Gain Tuning","authors":"Mengmeng Duan;Shanying Zhu;Ziwen Yang;Cailian Chen;Xinping Guan","doi":"10.1109/TSIPN.2025.3574852","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3574852","url":null,"abstract":"In this paper, we investigate the distributed optimization problem for heterogeneous linear multi-agent systems with unknown disturbances. To solve this problem, we propose a distributed controller design framework, which reduces the controller design for heterogeneous linear multi-agent systems to the stabilizer design for first-order multi-agent systems. By this framework, we propose two kinds of dynamic controllers under the strong convexity of the objective function and the restricted secant inequality condition, respectively. Based on the optimal condition and singular perturbation analysis technique, we prove that the system converges to the optimal state if the disturbances tend to be constant or vary slowly. To further optimize the performance criterion under system stability and input constraint, we provide an optimal gain tuning algorithm such that the system stability, optimality and feasibility are simultaneously achieved. Numerical examples are provided to illustrate the effectiveness of the theoretical results.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"563-576"},"PeriodicalIF":3.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272947","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