检索结果-内蒙古大学图书馆

3-D distributed Localization With Mixed Local Relative Measurements

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 5869-5881页

作者： Fang, Xu Li, Xiaolei Xie, Lihua Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

This paper studies 3-D distributed network localization using mixed types of local relative measurements. Each node holds a local coordinate frame without a common orientation and can only measure one type of information (relative position, distance, relative bearing, angle, or ratio-of-distance measurements) about its neighboring nodes in its local coordinate frame. A novel rigidity-theory-based distributed localization is developed to overcome the challenge due to the absence of a global coordinate frame. The main idea is to construct displacement constraints for the positions of the nodes by using mixed local relative measurements. Then, a linear distributed localization algorithm is proposed for each free node to estimate its position by solving the displacement constraints. The algebraic condition and graph condition are obtained to guarantee the global convergence of the proposed distributed localization algorithm.

关键词： Position measurement Displacement measurement Rigidity Signal processing algorithms Coordinate measuring machines Convergence distributed algorithms distributed localization sensor network mixed measurements network localizability 3-D space

来源：评论

学校读者我要写书评

暂无评论

DP-ADMM: ADMM-Based distributed Learning With Differential Privacy

引用

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2020年 15卷 1002-1012页

作者： Huang, Zonghao Hu, Rui Guo, Yuanxiong Chan-Tin, Eric Gong, Yanmin Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Univ Texas San Antonio Dept Elect & Comp Engn San Antonio TX 78249 USA Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX 78249 USA Loyola Univ Chicago Dept Comp Sci Chicago IL 60660 USA

Alternating direction method of multipliers (ADMM) is a widely used tool for machine learning in distri-buted settings where a machine learning model is trained over distributed data sources through an interactive process of local computation and message passing. Such an iterative process could cause privacy concerns of data owners. The goal of this paper is to provide differential privacy for ADMM-based distributed machine learning. Prior approaches on differentially private ADMM exhibit low utility under high privacy guarantee and assume the objective functions of the learning problems to be smooth and strongly convex. To address these concerns, we propose a novel differentially private ADMM-based distributed learning algorithm called DP-ADMM, which combines an approximate augmented Lagrangian function with time-varying Gaussian noise addition in the iterative process to achieve higher utility for general objective functions under the same differential privacy guarantee. We also apply the moments accountant method to analyze the end-to-end privacy loss. The theoretical analysis shows that the DP-ADMM can be applied to a wider class of distributed learning problems, is provably convergent, and offers an explicit utility-privacy tradeoff. To our knowledge, this is the first paper to provide explicit convergence and utility properties for differentially private ADMM-based distributed learning algorithms. The evaluation results demonstrate that our approach can achieve good convergence and model accuracy under high end-to-end differential privacy guarantee.

关键词： Machine learning ADMM distributed algorithms privacy differential privacy and moments accountant

来源：评论

学校读者我要写书评

暂无评论

Massively distributed Graph Distances

引用

IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2020年 6卷 667-683页

作者： Moharrer, Armin Gao, Jasmin Wang, Shikun Bento, Jose Ioannidis, Stratis Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08544 USA Boston Coll Dept Comp Sci Chestnut Hill MA 02467 USA

Graph distance (or similarity) scores are used in several graph mining tasks, including anomaly detection, nearest neighbor and similarity search, pattern recognition, transfer learning, and clustering. Graph distances that are metrics and, in particular, satisfy the triangle inequality, have theoretical and empirical advantages. Well-known graph distances that are metrics include the chemical or the Chartrand-Kubiki-Shultz (CKS) distances. Unfortunately, both are computationally intractable. Recent efforts propose using convex relaxations of the chemical and CKS distances. Though distance computation becomes a convex optimization problem under these relaxations, the number of variables is quadratic in the graph size;this makes traditional optimization algorithms prohibitive even for small graphs. We propose a distributed method formassively parallelizing this problem using the Alternating Directions Method of Multipliers (ADMM). Our solution uses a novel, distributed bisection algorithm for computing a p-norm proximal operator as a building block. We demonstrate its scalability by conducting experiments over multiple parallel environments.

关键词： ADMM distributed algorithms graph matching optimization

来源：评论

学校读者我要写书评

暂无评论

Resilient distributed Optimization Algorithm Against Adversarial Attacks

引用

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第10期65卷 4308-4315页

作者： Zhao, Chengcheng He, Jianping Wang, Qing-Guo Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Univ Johannesburg Fac Engn & Built Environm Inst Intelligent Syst ZA-2006 Johannesburg South Africa

As the cyber-attack is becoming one of the most challenging threats faced by cyber-physical systems, investigating the effect of cyber-attacks on distributed optimization and designing resilient algorithms are of both theoretical merits and practical values. Most existing works are established on the assumption that the maximum tolerable number of attacks, which depends on the network connectivity, is known by all normal agents. All normal agents will use the maximum number of attacks to decide whether the received information will be used for iterations. In this article, we relax this assumption and propose a novel resilient distributed optimization algorithm. The proposed algorithm exploits the trusted agents which cannot be compromised by adversarial attacks and form a connected dominating set in the original graph to constrain effects of adversarial attacks. It is shown that local variables of all normal and trusted agents converge to the same value under the proposed algorithm. Further, the final solution belongs to the convex set of minimizers of the weighted average of local cost functions of all trusted agents. The upper bound of the distance between the final solution and the optimal one has also been provided. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.

关键词： Cost function distributed algorithms Security Automation Performance analysis Consensus algorithm Adversarial attacks connected dominating set distributed optimization resilient algorithm trusted agents

来源：评论

学校读者我要写书评

暂无评论

Optimizing Content Placement and Delivery in Wireless distributed Cache Systems Through Belief Propagation

引用

IEEE ACCESS 2020年 8卷 100684-100701页

作者： Chuan, Jianbin Bai, Bo Wu, Xuewei Zhang, Hongming Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Huawei Technol Co Ltd Future Network Theory Lab Labs 2012 Hong Kong Peoples R China Beijing Univ Posts & Telecommun Sch Software Engn Beijing 100876 Peoples R China

Wireless distributed cache system (WDCS) is a promising technique to offload traffic and improve energy efficiency in content-centric networks. In this paper, the content sharing problem is considered by minimizing the average energy consumption for content caching and delivering in device-to-device (D2D) enabled cellular networks. The problem is formulated as a joint optimization problem of multiple interactive variables, which is NP-hard. In order to solve this problem, we first decompose the original problem into three subproblems, which are content helper (CH) selection, content caching, and content delivering. Specifically, in CH selection, we propose a PageRank based distributed algorithm to estimate the centrality of mobile users (MUs) by jointly analyzing their social relationships and channel conditions. Based on the selected CHs, the content caching and content delivering are formulated into two weighted matching problems. Two belief propagation (BP) based distributed algorithms are then proposed by considering content popularity and channel states to solve the aforementioned problems. Furthermore, based on the correlation of the three subproblems, a heuristic-based alternating iterative optimization method (HAIOM) is proposed to improve the average energy consumption performance of the content delivering of the WDCS. Simulation results show that by jointly optimizing the CH selection, content caching, and content delivering, the proposed scheme is capable of reducing the average energy consumption and the average content delivery delay, as well as of increasing the caching hit ratio.

关键词： Device-to-device communication Wireless communication Optimization Delays Energy consumption distributed algorithms Belief propagation Wireless distributed cache system content caching and delivering device-to-device communications belief propagation distributed algorithms

来源：评论

学校读者我要写书评

暂无评论

distributed Nash Equilibrium Seeking for Aggregative Games With Nonlinear Dynamics Under External Disturbances

引用

IEEE TRANSACTIONS ON CYBERNETICS 2020年第12期50卷 4876-4885页

作者： Zhang, Yawei Liang, Shu Wang, Xinghu Ji, Haibo Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Univ Sci & Technol Beijing Key Lab Knowledge Automat Ind Proc Minist Educ Sch Automat & Elect Engn Beijing 100083 Peoples R China

In this paper, we study the distributed Nash equilibrium (NE) seeking problem for a class of aggregative games with players described by uncertain perturbed nonlinear dynamics. To seek the NE, each player needs to construct a distributed algorithm based on information of its cost function and the exchanging information obtained from its neighbors. By combining the internal model principle and the average consensus technique, we propose a distributed gradient-based algorithm for the players. This paper not only assures the NE seeking of aggregative games but also achieves the disturbance rejection of external disturbances.

关键词： Games Heuristic algorithms Nonlinear dynamical systems distributed algorithms Nash equilibrium Cost function Automation Aggregative games distributed algorithms external disturbances Nash equilibrium (NE) nonlinear dynamics

来源：评论

学校读者我要写书评

暂无评论

A distributed Algorithm For Almost-Nash Equilibria of Average Aggregative Games With Coupling Constraints

引用

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2020年第2期7卷 770-782页

作者： Parise, Francesca Gentile, Basilio Lygeros, John MIT Lab Informat & Decis Syst 77 Massachusetts Ave Cambridge MA 02139 USA Circuit Mind Ltd London SE1 0HS England Swiss Fed Inst Technol Automat Control Lab CH-8092 Zurich Switzerland

We consider the framework of average aggregative games, where the cost function of each agent depends on his own strategy and on the average population strategy. We focus on the case in which the agents are coupled not only via their cost functions, but also via a shared constraint coupling their strategies. We propose a distributed algorithm that achieves an e-Nash equilibrium by requiring only local communications of the agents, as specified by a sparse communication network. The proof of convergence of the algorithm relies on the auxiliary class of network aggregative games. We apply our theoretical findings to a multimarket Cournot game with transportation costs and maximum market capacity.

关键词： Aggregative games coupling constraints generalized Nash equilibrium distributed algorithms large-scale systems Cournot game

来源：评论

学校读者我要写书评

暂无评论

distributed best response dynamics for Nash equilibrium seeking in potential games

引用

Control Theory and Technology 2020年第3期18卷 324-332页

作者： Shijie HUANG Peng YI Key Lab of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100190China Department of Control Science&Engineering Tongji UniversityShanghai 201804China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 201804China

In this paper,we consider distributed Nash equilibrium(NE)seeking in potential games over a multi-agent network,where each agent can not observe the actions of all its *** on the best response dynamics,we design a distributed NE seeking algorithm by incorporating the non-smooth finite-time average tracking dynamics,where each agent only needs to know its own action and exchange information with its neighbours through a communication *** give a sufficient condition for the Lipschitz continuity of the best response mapping for potential games,and then prove the convergence of the proposed algorithm based on the Lyapunov *** simulations are given to verify the resultandillustrate the effectiveness of the algorithm.

关键词： distributed algorithms Nash equilibrium seeking best response dynamics non-smooth finite-time tracking dynamics potential games

来源：评论

学校读者我要写书评

暂无评论

Asynchronous distributed Learning From Constraints

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020年第10期31卷 4367-4373页

作者： Farina, Francesco Melacci, Stefano Garulli, Andrea Giannitrapani, Antonio Univ Siena Dipartimento Ingn Informaz & Sci Matemat I-53100 Siena Italy

In this brief, the extension of the framework of Learning from Constraints (LfC) to a distributed setting where multiple parties, connected over the network, contribute to the learning process is studied. LfC relies on the generic notion of "constraint" to inject knowledge into the learning problem, and, due to its generality, it deals with possibly nonconvex constraints, enforced either in a hard or soft way. Motivated by recent progresses in the field of distributed and constrained nonconvex optimization, we apply the (distributed) asynchronous method of multipliers (ASYMM) to LfC. The study shows that such a method allows us to support scenarios where selected constraints (i.e., knowledge), data, and outcomes of the learning process can be locally stored in each computational node without being shared with the rest of the network, opening the road to further investigations into privacy-preserving LfC. Constraints act as a bridge between what is shared over the net and what is private to each node, and no central authority is required. We demonstrate the applicability of these ideas in two distributed real-world settings in the context of digit recognition and document classification.

关键词： Knowledge engineering distributed databases Optimization Task analysis Learning systems Bridges Neural networks Artificial neural networks distributed algorithms optimization semisupervised learning

来源：评论

学校读者我要写书评

暂无评论

Experimental Evaluation of Synchronous distributed Dominating Set algorithms 27

Experimental Evaluation of Synchronous Distributed Dominatin...

引用

27th Signal Processing and Communications Applications Conference (SIU)

作者： Tosun, Mustafa Dagdeviren, Orhan Ege Univ Int Comp Inst Izmir Turkey

ISBN: (纸本)9781728119045

Dominating sets are one of the most important graph theoretic structures used in clustering and routing protocols in wireless sensor networks. For this reason, many distributed dominating set algorithms have been studied by researchers. Since wireless sensor networks are usually composed of battery powered devices, it is important to minimize the resource consumption of dominating set algorithms. In this study, experimental performance of four synchronous distributed dominating set algorithms including 1-hop span-based, 2-hop span-based, random and identity (ID)-based are measured in SimPy environment in terms of time, message, cluster size and energy per node. While performing these tests, the node count and the average node degree in the wireless sensor networks are changed and a large number of measurements are taken to compare the algorithms. As a result of the detailed experiments, 2-hop span-based algorithm has the best results in terms of set size and the ID-based algorithm has the best results in terms of resource utilization.

关键词： dominating set wireless sensor networks distributed algorithms performance evaluation graph theory

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：