Pub Date : 2025-07-10DOI: 10.1109/TSIPN.2025.3588094
Yan Li;Min Gao;Lijuan Zha;Jinliang Liu;Engang Tian;Chen Peng
This paper addresses the problem of observer-based $H_{infty }$ synchronization control for singularly perturbed multiweighted complex networks (SPMCNs) with communication constraints and cyberattack threats. Firstly, given the limited communication bandwidth, a stochastic communication (SC) protocol is employed to deal with the potential data collision in each node of SPMCNs incurred by the mismatch between traffic load and resource availability. The SC protocol is specifically depicted by a Markov chain with partially known transition probabilities to improve its applicability. Then, the cybersecurity for SPMCNs is investigated, and the focus is concentrated on deception attacks due to they pose significant risks by maliciously tampering with sensitive information. Based on modeling the behavior of the considered deception attacks, observer-assisted synchronization controllers with undetermined gains are designed and an augmented synchronization error system is established. Subsequently, the stability with guaranteed $H_{infty }$ control performance of the constructed system is analyzed, and then a feasible algorithm for determining the gains of the desired observers and controllers is provided. Finally, simulations are conducted based on an urban public traffic network to validate the efficiency and practicability of the proposed synchronization control scheme.
{"title":"Secure Observer-Based $H_{infty }$ Synchronization for Singularly Perturbed Multiweighted Complex Networks With Stochastic Communication Protocol","authors":"Yan Li;Min Gao;Lijuan Zha;Jinliang Liu;Engang Tian;Chen Peng","doi":"10.1109/TSIPN.2025.3588094","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3588094","url":null,"abstract":"This paper addresses the problem of observer-based <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> synchronization control for singularly perturbed multiweighted complex networks (SPMCNs) with communication constraints and cyberattack threats. Firstly, given the limited communication bandwidth, a stochastic communication (SC) protocol is employed to deal with the potential data collision in each node of SPMCNs incurred by the mismatch between traffic load and resource availability. The SC protocol is specifically depicted by a Markov chain with partially known transition probabilities to improve its applicability. Then, the cybersecurity for SPMCNs is investigated, and the focus is concentrated on deception attacks due to they pose significant risks by maliciously tampering with sensitive information. Based on modeling the behavior of the considered deception attacks, observer-assisted synchronization controllers with undetermined gains are designed and an augmented synchronization error system is established. Subsequently, the stability with guaranteed <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> control performance of the constructed system is analyzed, and then a feasible algorithm for determining the gains of the desired observers and controllers is provided. Finally, simulations are conducted based on an urban public traffic network to validate the efficiency and practicability of the proposed synchronization control scheme.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"767-779"},"PeriodicalIF":3.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687811","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}
Pub Date : 2025-07-10DOI: 10.1109/TSIPN.2025.3587394
Yu Chen;Wei Wang;Zidong Wang;Chunyan Han;Shuxin Du
In this paper, the recursive state estimation problem is investigated for a class of two-dimensional networked shift-varying systems with asynchronous measurement delays and non-logarithmic sensor resolution. A new soft measurement model with asynchronous delays is developed to deal with the inaccurate measurements caused by the non-logarithmic sensor resolution, and a recombination method is proposed to tackle the difficulties induced by the asynchronous measurement delays. The purpose of this paper is to design a finite-horizon filter such that under the joint effects of asynchronous measurement delays and non-logarithmic sensor resolution, an upper bound for the filtering error covariance is ensured and then minimized by appropriately designing the gain parameters. Some sufficient conditions are established to guarantee the boundedness of the proposed filtering algorithm. Finally, two illustrative examples are presented to showcase the effectiveness of the proposed finite-horizon filtering scheme.
{"title":"Finite-Horizon Filtering for Networked 2-D Systems With Asynchronous Measurement Delays Under Non-Logarithmic Sensor Resolution","authors":"Yu Chen;Wei Wang;Zidong Wang;Chunyan Han;Shuxin Du","doi":"10.1109/TSIPN.2025.3587394","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3587394","url":null,"abstract":"In this paper, the recursive state estimation problem is investigated for a class of two-dimensional networked shift-varying systems with asynchronous measurement delays and non-logarithmic sensor resolution. A new soft measurement model with asynchronous delays is developed to deal with the inaccurate measurements caused by the non-logarithmic sensor resolution, and a recombination method is proposed to tackle the difficulties induced by the asynchronous measurement delays. The purpose of this paper is to design a finite-horizon filter such that under the joint effects of asynchronous measurement delays and non-logarithmic sensor resolution, an upper bound for the filtering error covariance is ensured and then minimized by appropriately designing the gain parameters. Some sufficient conditions are established to guarantee the boundedness of the proposed filtering algorithm. Finally, two illustrative examples are presented to showcase the effectiveness of the proposed finite-horizon filtering scheme.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"805-820"},"PeriodicalIF":3.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716138","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}
Pub Date : 2025-07-10DOI: 10.1109/TSIPN.2025.3587440
Reza Mirzaeifard;Diyako Ghaderyan;Stefan Werner
The rise of internet-of-things (IoT) systems has led to the generation of vast and high-dimensional data across distributed edge devices, often requiring sparse modeling techniques to manage model complexity efficiently. In these environments, quantile regression offers a robust alternative to mean-based models by capturing conditional distributional behavior, which is particularly useful under heavy-tailed noise or heterogeneous data. However, penalized quantile regression in federated learning (FL) remains challenging due to the non-smooth nature of the quantile loss and the non-convex, non-smooth penalties such as MCP and SCAD used for sparsity. To address this gap, we propose the Federated Smoothing Proximal Gradient (FSPG) algorithm, which integrates a smoothing technique into the proximal gradient framework to enable effective, stable, and theoretically guaranteed optimization in decentralized settings. FSPG guarantees monotonic reduction in the objective function and achieves faster convergence than existing methods. We further extend FSPG to handle partial client participation (PCP-FSPG), making the algorithm robust to intermittent node availability by adaptively updating local parameters based on client activity. Extensive experiments validate that FSPG and PCP-FSPG achieve superior accuracy, convergence behavior, and variable selection performance compared to existing baselines, demonstrating their practical utility in real-world federated applications.
{"title":"Federated Smoothing Proximal Gradient for Quantile Regression With Non-Convex Penalties","authors":"Reza Mirzaeifard;Diyako Ghaderyan;Stefan Werner","doi":"10.1109/TSIPN.2025.3587440","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3587440","url":null,"abstract":"The rise of internet-of-things (IoT) systems has led to the generation of vast and high-dimensional data across distributed edge devices, often requiring sparse modeling techniques to manage model complexity efficiently. In these environments, quantile regression offers a robust alternative to mean-based models by capturing conditional distributional behavior, which is particularly useful under heavy-tailed noise or heterogeneous data. However, penalized quantile regression in federated learning (FL) remains challenging due to the non-smooth nature of the quantile loss and the non-convex, non-smooth penalties such as MCP and SCAD used for sparsity. To address this gap, we propose the Federated Smoothing Proximal Gradient (FSPG) algorithm, which integrates a smoothing technique into the proximal gradient framework to enable effective, stable, and theoretically guaranteed optimization in decentralized settings. FSPG guarantees monotonic reduction in the objective function and achieves faster convergence than existing methods. We further extend FSPG to handle partial client participation (PCP-FSPG), making the algorithm robust to intermittent node availability by adaptively updating local parameters based on client activity. Extensive experiments validate that FSPG and PCP-FSPG achieve superior accuracy, convergence behavior, and variable selection performance compared to existing baselines, demonstrating their practical utility in real-world federated applications.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"696-710"},"PeriodicalIF":3.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680838","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}
Pub Date : 2025-07-10DOI: 10.1109/TSIPN.2025.3587405
Hai-Xiu Xie;Jin-Xi Zhang;Yuanwei Jing;Georgi M. Dimirovski
This paper is concerned with the high-performance leader-following problem for the high-order heterogeneous nonlinear multiagent systems under a directed graph. It is focused on the cases where the system dynamics of each agent is totally unknown; the system nonlinearities are radially unbounded; the control directions may switch between positive and negative. They render the existing distributed control solutions infeasible. To conquer this challenge, a distributed model-free robust funnel control strategy is put forward in this paper. It achieves output synchronization with the predefined settling time and accuracy even after the control direction switching. Moreover, it is static, continuous and strikingly simple, without invoking the Nussbaum-gain technique, the sliding-mode control method, or the tools for identification, approximation, estimation, filtering, etc. The theoretical findings are illustrated by a comparative simulation on a team of inverted pendulum systems.
{"title":"Distributed Model-Free Funnel Control of Nonlinear Multiagent Systems With Switching Control Directions","authors":"Hai-Xiu Xie;Jin-Xi Zhang;Yuanwei Jing;Georgi M. Dimirovski","doi":"10.1109/TSIPN.2025.3587405","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3587405","url":null,"abstract":"This paper is concerned with the high-performance leader-following problem for the high-order heterogeneous nonlinear multiagent systems under a directed graph. It is focused on the cases where the system dynamics of each agent is totally unknown; the system nonlinearities are radially unbounded; the control directions may switch between positive and negative. They render the existing distributed control solutions infeasible. To conquer this challenge, a distributed model-free robust funnel control strategy is put forward in this paper. It achieves output synchronization with the predefined settling time and accuracy even after the control direction switching. Moreover, it is static, continuous and strikingly simple, without invoking the Nussbaum-gain technique, the sliding-mode control method, or the tools for identification, approximation, estimation, filtering, etc. The theoretical findings are illustrated by a comparative simulation on a team of inverted pendulum systems.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"711-723"},"PeriodicalIF":3.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680851","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}
This paper addresses the problem of single-scan bearing-only measurement matching for multi-targets in three-dimensional space over sensor networks. Since bearing-only measurements lack one dimension of information about targets, the corresponding matching problem cannot be solved using existing data association algorithms designed for three-dimensional measurements. Also, single-scan matching requires algorithms to be efficient without relying on prior information. These challenges make the single-scan bearing-only measurement matching problem in sensor networks an open research question. To tackle this issue, this paper proposes a scalable matching algorithm for bearing-only sensor networks using a two-step approach. First, a cost function is designed to quantitatively evaluate the disparity between pairs of bearing-only measurements, incorporating analytic and geometric attributes. Then, a cost-function-based algorithm for matching measurements between two bearing-only sensors is developed. Finally, by combining the proposed two-sensor algorithm with the Minimum Spanning Tree technique, a scalable matching algorithm for bearing-only sensor networks is constructed. The effectiveness of the algorithm is demonstrated through simulation results under several typical scenarios.
{"title":"On Scalable Matching Algorithm for Multi-Targets Over Bearing-Only Sensor Networks","authors":"Shenghua Hu;Wenchao Xue;Biqiang Mu;Haitao Fang;Yang Xu","doi":"10.1109/TSIPN.2025.3587409","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3587409","url":null,"abstract":"This paper addresses the problem of single-scan bearing-only measurement matching for multi-targets in three-dimensional space over sensor networks. Since bearing-only measurements lack one dimension of information about targets, the corresponding matching problem cannot be solved using existing data association algorithms designed for three-dimensional measurements. Also, single-scan matching requires algorithms to be efficient without relying on prior information. These challenges make the single-scan bearing-only measurement matching problem in sensor networks an open research question. To tackle this issue, this paper proposes a scalable matching algorithm for bearing-only sensor networks using a two-step approach. First, a cost function is designed to quantitatively evaluate the disparity between pairs of bearing-only measurements, incorporating analytic and geometric attributes. Then, a cost-function-based algorithm for matching measurements between two bearing-only sensors is developed. Finally, by combining the proposed two-sensor algorithm with the Minimum Spanning Tree technique, a scalable matching algorithm for bearing-only sensor networks is constructed. The effectiveness of the algorithm is demonstrated through simulation results under several typical scenarios.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"724-739"},"PeriodicalIF":3.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687795","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}
Complex networked systems driven by latent inputs are common in fields like neuroscience, finance, and engineering. A key inference problem here is to learn edge connectivity from node outputs (potentials). We focus on systems governed by steady-state linear conservation laws: $X_{t} = {L^{ast }}Y_{t}$, where $X_{t}, Y_{t} in mathbb {R}^{p}$ denote inputs and potentials, respectively, and the sparsity pattern of the $p times p$ Laplacian $L^{ast }$ encodes the edge structure. Assuming $X_{t}$ to be a wide-sense stationary stochastic process with a known spectral density matrix, we learn the support of $L^{ast }$ from temporally correlated samples of $Y_{t}$ via an $ell _{1}$-regularized Whittle’s maximum likelihood estimator (MLE). The regularization is particularly useful for learning large-scale networks in the high-dimensional setting where the network size $p$ significantly exceeds the number of samples $n$. We show that the MLE problem is strictly convex, admitting a unique solution. Under a novel mutual incoherence condition and certain sufficient conditions on $(n, p, d)$, we show that the ML estimate recovers the sparsity pattern of $L^ast$ with high probability, where $d$ is the maximum degree of the graph underlying $L^{ast }$. We provide recovery guarantees for $L^ast$ in element-wise maximum, Frobenius, and operator norms. Finally, we complement our theoretical results with several simulation studies on synthetic and benchmark datasets, including engineered systems (power and water networks), and real-world datasets from neural systems (such as the human brain).
{"title":"Learning Networks From Wide-Sense Stationary Stochastic Processes","authors":"Anirudh Rayas;Jiajun Cheng;Rajasekhar Anguluri;Deepjyoti Deka;Gautam Dasarathy","doi":"10.1109/TSIPN.2025.3583488","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3583488","url":null,"abstract":"Complex networked systems driven by latent inputs are common in fields like neuroscience, finance, and engineering. A key inference problem here is to learn edge connectivity from node outputs (potentials). We focus on systems governed by steady-state linear conservation laws: <inline-formula><tex-math>$X_{t} = {L^{ast }}Y_{t}$</tex-math></inline-formula>, where <inline-formula><tex-math>$X_{t}, Y_{t} in mathbb {R}^{p}$</tex-math></inline-formula> denote inputs and potentials, respectively, and the sparsity pattern of the <inline-formula><tex-math>$p times p$</tex-math></inline-formula> Laplacian <inline-formula><tex-math>$L^{ast }$</tex-math></inline-formula> encodes the edge structure. Assuming <inline-formula><tex-math>$X_{t}$</tex-math></inline-formula> to be a wide-sense stationary stochastic process with a known spectral density matrix, we learn the support of <inline-formula><tex-math>$L^{ast }$</tex-math></inline-formula> from temporally correlated samples of <inline-formula><tex-math>$Y_{t}$</tex-math></inline-formula> via an <inline-formula><tex-math>$ell _{1}$</tex-math></inline-formula>-regularized Whittle’s maximum likelihood estimator (MLE). The regularization is particularly useful for learning large-scale networks in the high-dimensional setting where the network size <inline-formula><tex-math>$p$</tex-math></inline-formula> significantly exceeds the number of samples <inline-formula><tex-math>$n$</tex-math></inline-formula>. We show that the MLE problem is strictly convex, admitting a unique solution. Under a novel mutual incoherence condition and certain sufficient conditions on <inline-formula><tex-math>$(n, p, d)$</tex-math></inline-formula>, we show that the ML estimate recovers the sparsity pattern of <inline-formula><tex-math>$L^ast$</tex-math></inline-formula> with high probability, where <inline-formula><tex-math>$d$</tex-math></inline-formula> is the maximum degree of the graph underlying <inline-formula><tex-math>$L^{ast }$</tex-math></inline-formula>. We provide recovery guarantees for <inline-formula><tex-math>$L^ast$</tex-math></inline-formula> in element-wise maximum, Frobenius, and operator norms. Finally, we complement our theoretical results with several simulation studies on synthetic and benchmark datasets, including engineered systems (power and water networks), and real-world datasets from neural systems (such as the human brain).","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"655-669"},"PeriodicalIF":3.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634609","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}
Pub Date : 2025-06-26DOI: 10.1109/TSIPN.2025.3583355
Shujuan Wei;Huijun Tang;Pengfei Jiao;Huaming Wu
Graph-structured data is an important data form that is widely used in the real world. It can effectively and abstractly express entities in information and the relationships between entities. The appearance of Graph Neural Networks (GNNs) provides a potent tool for dealing with nonlinear data structures, which mainly learns node representation through information propagation and aggregation on the nodes in the graph. However, existing GNNs fail to adequately and efficiently integrate the topological structure of the network and node features during information propagation, resulting in an insufficient capture of the complex influence relationships between nodes. The limitation constrains the expression ability of the models and seriously impacts their performance in node classification tasks. To overcome this issue, we propose a Gravitation-based Graph Attention Network (GGAT) for node classification. Firstly, we define a novel similarity measurement method based on the formula of universal gravitation, which combines node information entropy and spatial distance. This method overcomes the limitation of existing similarity measurements that focus solely on the topological structure or node features, achieving a more comprehensive similarity assessment. Then, we apply it to the graph attention network as a novel attention mechanism. Compared with the traditional attention mechanisms based on learning, our proposed mechanism not only thoroughly considers the topological structure and node features to allocate the weights of neighbor nodes but also makes the calculation of attention weights more transparent with an intuitive physical significance, thereby improving the stability and interpretability of the model. Finally, the experiments are carried out on various real datasets, and the results show that GGAT is superior to the existing popular models in node classification performance.
{"title":"GGAT: Gravitation-Based Graph Attention Networks","authors":"Shujuan Wei;Huijun Tang;Pengfei Jiao;Huaming Wu","doi":"10.1109/TSIPN.2025.3583355","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3583355","url":null,"abstract":"Graph-structured data is an important data form that is widely used in the real world. It can effectively and abstractly express entities in information and the relationships between entities. The appearance of Graph Neural Networks (GNNs) provides a potent tool for dealing with nonlinear data structures, which mainly learns node representation through information propagation and aggregation on the nodes in the graph. However, existing GNNs fail to adequately and efficiently integrate the topological structure of the network and node features during information propagation, resulting in an insufficient capture of the complex influence relationships between nodes. The limitation constrains the expression ability of the models and seriously impacts their performance in node classification tasks. To overcome this issue, we propose a Gravitation-based Graph Attention Network (GGAT) for node classification. Firstly, we define a novel similarity measurement method based on the formula of universal gravitation, which combines node information entropy and spatial distance. This method overcomes the limitation of existing similarity measurements that focus solely on the topological structure or node features, achieving a more comprehensive similarity assessment. Then, we apply it to the graph attention network as a novel attention mechanism. Compared with the traditional attention mechanisms based on learning, our proposed mechanism not only thoroughly considers the topological structure and node features to allocate the weights of neighbor nodes but also makes the calculation of attention weights more transparent with an intuitive physical significance, thereby improving the stability and interpretability of the model. Finally, the experiments are carried out on various real datasets, and the results show that GGAT is superior to the existing popular models in node classification performance.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"630-640"},"PeriodicalIF":3.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581557","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}
Pub Date : 2025-06-19DOI: 10.1109/TSIPN.2025.3581086
Yang Yang;Yiwei Yang;Duo Ye
A secure bipartite consensus control strategy with an event-trigger-based exclusion is reported for uncertain nonlinear multi-agent systems with malicious agents in a signed directed graph. For malicious agents, an event-triggered exclusion algorithm is proposed to judge suspicious ones. Then, a distributed dynamic surface control is employed with relative secure agents to achieve bipartite consensus. The dynamic event-triggered condition is constructed based on event-triggered errors and dynamic surface errors for reduction of communication load. With the help of Lyapunov functions, it is proven that the consensus errors are ultimately bounded and converge to an adjustable neighborhood of the origin. Finally, two simulation results are illustrated to verify the feasibility of the secure control strategy.
{"title":"Secure Bipartite Consensus With Event-Trigger-Based Exclusion for Nonlinear Multi-Agent Systems Under Malicious Attacks","authors":"Yang Yang;Yiwei Yang;Duo Ye","doi":"10.1109/TSIPN.2025.3581086","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3581086","url":null,"abstract":"A secure bipartite consensus control strategy with an event-trigger-based exclusion is reported for uncertain nonlinear multi-agent systems with malicious agents in a signed directed graph. For malicious agents, an event-triggered exclusion algorithm is proposed to judge suspicious ones. Then, a distributed dynamic surface control is employed with relative secure agents to achieve bipartite consensus. The dynamic event-triggered condition is constructed based on event-triggered errors and dynamic surface errors for reduction of communication load. With the help of Lyapunov functions, it is proven that the consensus errors are ultimately bounded and converge to an adjustable neighborhood of the origin. Finally, two simulation results are illustrated to verify the feasibility of the secure control strategy.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1228-1237"},"PeriodicalIF":3.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141624","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}
Pub Date : 2025-06-12DOI: 10.1109/TSIPN.2025.3579245
Olusola T. Odeyomi;Temitayo O. Olowu;Opeyemi Ajibuwa;Abdollah Homaifar
Decentralized multi-agent systems are well known for their ability to model complex systems, such as smart grids, autonomous vehicles, etc. Many decentralized multi-agent systems can be modeled as cluster-based non-cooperative games in which agents within a cluster have selfish interests different from those of agents in other clusters. In this paper, we consider a cluster-based non-cooperative game for multi-agent systems in the presence of Byzantine attacks. This is an area of research yet to be explored in non-cooperative games. Therefore, we propose a novel Byzantine-resilient online mirror descent-based decentralized Nash algorithm. We assume that the loss function is time-varying and non-convex. Also, the agents within each cluster form an unbalanced graph network. Our theoretical and simulation results show that the proposed algorithm is resilient against Byzantine attacks and computationally efficient.
{"title":"Online Non-Convex Non-Cooperative Cluster-Based Games With Byzantine Resiliency in Decentralized Multi-Agent Systems","authors":"Olusola T. Odeyomi;Temitayo O. Olowu;Opeyemi Ajibuwa;Abdollah Homaifar","doi":"10.1109/TSIPN.2025.3579245","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3579245","url":null,"abstract":"Decentralized multi-agent systems are well known for their ability to model complex systems, such as smart grids, autonomous vehicles, etc. Many decentralized multi-agent systems can be modeled as cluster-based non-cooperative games in which agents within a cluster have selfish interests different from those of agents in other clusters. In this paper, we consider a cluster-based non-cooperative game for multi-agent systems in the presence of Byzantine attacks. This is an area of research yet to be explored in non-cooperative games. Therefore, we propose a novel Byzantine-resilient online mirror descent-based decentralized Nash algorithm. We assume that the loss function is time-varying and non-convex. Also, the agents within each cluster form an unbalanced graph network. Our theoretical and simulation results show that the proposed algorithm is resilient against Byzantine attacks and computationally efficient.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"752-766"},"PeriodicalIF":3.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687641","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}
This paper proposes a reinforcement learning approach to the output synchronization problem for heterogeneous leader-follower multi-agent systems, where the system dynamics of all agents are completely unknown. First, to solve the challenge caused by unknown dynamics of the leader, we develop an experience-replay learning method to estimate the leader’s dynamics, which only uses the leader’s past state and output information as training data. Second, based on the newly estimated leader’s dynamics, we design an event-triggered observer for each follower to estimate the leader’s state and output. Furthermore, the experience-replay learning method and the event-triggered leader observer are co-designed, which ensures the convergence and Zeno behavior exclusion. Subsequently, to free the followers from reliance on system dynamics, a data-driven adaptive dynamic programming (ADP) method is presented to iteratively derive the optimal control gains, based on which we design a policy iteration (PI) algorithm for output synchronization. Finally, the proposed algorithm’s performance is validated through a simulation.
{"title":"Adaptive Event-Triggered Output Synchronization of Heterogeneous Multiagent Systems: A Model-Free Reinforcement Learning Approach","authors":"Wenfeng Hu;Xuan Wang;Meichen Guo;Biao Luo;Tingwen Huang","doi":"10.1109/TSIPN.2025.3578759","DOIUrl":"https://doi.org/10.1109/TSIPN.2025.3578759","url":null,"abstract":"This paper proposes a reinforcement learning approach to the output synchronization problem for heterogeneous leader-follower multi-agent systems, where the system dynamics of all agents are completely unknown. First, to solve the challenge caused by unknown dynamics of the leader, we develop an experience-replay learning method to estimate the leader’s dynamics, which only uses the leader’s past state and output information as training data. Second, based on the newly estimated leader’s dynamics, we design an event-triggered observer for each follower to estimate the leader’s state and output. Furthermore, the experience-replay learning method and the event-triggered leader observer are co-designed, which ensures the convergence and Zeno behavior exclusion. Subsequently, to free the followers from reliance on system dynamics, a data-driven adaptive dynamic programming (ADP) method is presented to iteratively derive the optimal control gains, based on which we design a policy iteration (PI) algorithm for output synchronization. Finally, the proposed algorithm’s performance is validated through a simulation.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"604-616"},"PeriodicalIF":3.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524395","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}
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