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IEEE Conference on Decision and Control

作者： Tong Yao Shreyas Sundaram Elmore Family School of Electrical and Computer Engineering Purdue University

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We study robust mean estimation in an online and distributed scenario in the presence of adversarial data attacks. At each time step, each agent in a network receives a potentially corrupted data point, where the data points were originally independent and identically distributed samples of a random variable. We propose online and distributed algorithms for all agents to asymptotically estimate the mean. We provide the error-bound and the convergence properties of the estimates to the true mean under our algorithms. Based on the network topology, we further evaluate each agent’s trade-off in convergence rate between incorporating data from neighbors and learning with only local observations.

关键词： Network topology distributed databases Estimation Random variables distributed algorithms Convergence

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On the Impacts of Different Consistency Constraint Formulations for distributed Optimal Power Flow

On the Impacts of Different Consistency Constraint Formulati...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Rachel Harris Mohannad Alkhraijah David Huggins Daniel K. Molzahn School of Electrical and Computer Engineering Georgia Institute of Technology Georgia Tech Research Institute Georgia Institue of Technology

The optimal power flow (OPF) problem finds the least costly operating point which meets the power grid's operational limits and obeys physical power flow laws. Complementing today's centralized optimization paradigm, future power grids may rely on distributed optimization where multiple agents work together to determine an acceptable operating point. In distributed algorithms, local agents solve subproblems to optimize their region of the system and share data to achieve consistency with their neighboring agents' subproblems. This paper investigates how different methods of enforcing power flow consistency constraints between local areas in distributed optimal power flow impact convergence rate and a classifier's ability to detect malicious cyberattack. The distributed OPF problem is solved with the alternating direction method of multipliers (ADMM) algorithm. First, the ADMM algorithm's convergence rate is compared for three different consistency constraint formulations. Next, the paper considers a cyberattack in which the integrity of information shared between agents is compromised, causing the algorithm to exhibit unacceptable behavior. A support vector machine (SVM) classifier is trained to detect the presence of manipulated data from such cyberattacks. Results demonstrate that consistency constraint formulation impacts the classifier's detection performance; for certain formulations, detection is highly accurate.

关键词： Support vector machines Voltage Convex functions Classification algorithms Smart grids distributed algorithms Optimization

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Accelerated distributed Nesterov Gradient Descent

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第6期65卷 2566-2581页

作者： Qu, Guannan Li, Na Harvard Univ John A Paulson Sch Engn & Appl Sci Cambridge MA 02138 USA

This paper considers the distributed optimization problem over a network, where the objective is to optimize a global function formed by a sum of local functions, using only local computation and communication. We develop an accelerated distributed Nesterov gradient descent method. When the objective function is convex and L-smooth, we show that it achieves a O(1/t(1.4-epsilon)) convergence rate for all epsilon is an element of (0, 1.4). We also show the convergence rate can be improved to O(1/t(2)) if the objective function is a composition of a linear map and a strongly convex and smooth function. When the objective function is mu-strongly convex and L-smooth, we show that it achieves a linear convergence rate of O([1 - C(mu/L)(5/7)](t)), where L/mu is the condition number of the objective, and C > 0 is some constant that does not depend on L/mu.

关键词： Convergence Acceleration Convex functions Radio frequency Linear programming Gradient methods distributed algorithms multiagent systems optimization methods distributed optimization

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distributed Inequality Constrained Online Optimization for Unbalanced Digraphs using Row Stochastic Property

Distributed Inequality Constrained Online Optimization for U...

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IEEE Conference on Decision and Control

作者： Keishin Tada Naoki Hayashi Shigemasa Takai Graduate School of Engineering Osaka University Suita Osaka Japan Graduate School of Engineering Science Osaka University Toyonaka Osaka Japan

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this study, we discuss a primal-dual distributed algorithm for online convex optimization with a time-varying coupled constraint on unbalanced directed graphs. A group of agents exchanges the estimation variable for the dual optimizer and the scaling variable, which are used for compensating the unbalanced information flow. Then, each agent updates the primal and dual variables using the projected subgradient methods. We confirm that the regret of the cost function and the cumulative error of the constraint violation achieve sublinearity. A numerical example of a distributed economic dispatch problem demonstrates that the estimation of each agent approaches the optimal strategy under the coupled inequality constraint.

关键词： Economics Estimation Directed graphs Cost function Convex functions Linear matrix inequalities distributed algorithms

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Robust Dynamical Virtual Network Provisioning

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Chinese Journal of Electronics 2023年第1期22卷 1-4页

作者： Min Zhang Chunming Wu Yue Hang Qiang Yang Bin Wang Ming Jiang College of Computer Science and Technology Zhejiang University Hangzhou China College of Computer Science Hangzhou Dianzi University Hangzhou China Department 66377 Beijing China College of Electrical Engineering Zhejiang University Hangzhou China

Multiple virtual networks (VNs) sharing an underlying substrate network is considered a promising tool to diversify and reshape the future inter-networking paradigm. In this paper, based on the robust optimization theory, a robust dynamic approach is presented, which periodically identifies bandwidth allocation to VNs to work reasonable well for a range of traffic patterns over a period of time, rather than certain traffic pattern instance. This problem is formulated as a robust optimization problem using path-flow model aiming to compute the minimum-cost bandwidth allocation, and a distributed algorithm is proposed by using the primal decomposition method. The numerical result obtained from simulation experiments demonstrates the strength and the effectiveness of the proposed algorithm.

关键词： Heuristic algorithms Channel allocation Traffic control Dynamic scheduling Numerical models Resource management distributed algorithms

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Fast gathering despite a linear number of weakly Byzantine agents

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第14期36卷 e8055-e8055页

作者： Hirose, Jion Nakamura, Junya Ooshita, Fukuhito Inoue, Michiko Nara Inst Sci & Technol Grad Sch Sci & Technol Ikoma Japan Toyohashi Univ Technol Informat & Media Ctr Toyohashi Japan Fukui Univ Technol Dept Elect & Elect Engn Fukui Japan

In this work, we study the gathering problem to make multiple agents, who are initially scattered in arbitrary networks, meet at the same node. The network has f agents with unique identifiers (IDs), and k of them are weakly Byzantine agents that behave arbitrarily, except for falsifying their identifiers. These agents behave in synchronous rounds, and they may start an algorithm at different rounds. Each agent cannot leave information at a node. We propose herein a deterministic algorithm that efficiently achieves gathering with a simultaneous termination having a small number of non-Byzantine agents. The proposed algorithm concretely works in O(f|Lambda(all)|X(N)) rounds if the agents know the upper bound N on the number of nodes, and at least 7f+7 non-Byzantine agents exist, where |Lambda(all)| is the length of the largest ID among agents, and X(N) is the number of rounds required to explore any network composed of n nodes. The literature presents two efficient gathering algorithms with a simultaneous termination. The first algorithm assumes that agents know the number n of nodes and achieves the gathering in O(f|Lambda(all)|X(N)) rounds in the presence of any number of Byzantine agents, where |Lambda good| is the length of the largest ID among non-Byzantine agents. The second algorithm assumes both that agents know N and that at least 4f(2)+8f+4 non-Byzantine agents exist, and it achieves the gathering in O(f|Lambda(all)|X(N)) rounds. The proposed algorithm is faster than the first existing algorithm and requires fewer non-Byzantine agents than the second existing algorithm if n is given to agents. We propose herein a new technique to simulate a Byzantine consensus algorithm for synchronous message-passing systems on agent systems to reduce the number of agents.

关键词： Byzantine environments distributed algorithms gathering mobile agents

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Resilient Consensus-based Economic Dispatch Strategy for Power Security Monitoring 22

Resilient Consensus-based Economic Dispatch Strategy for Pow...

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22nd IEEE International Conference on Industrial Technology (ICIT)

作者： Zheng, Xiang Du, Qiwei Wang, Haiyuan Wang, Jingpei Quzhou Power Supply Co Quzhou Zhejiang Peoples R China State Grid Zhejiang Elect Power Co Ltd Hangzhou Zhejiang Peoples R China Zhejiang Univ Coll Control Sci & Engn 38 Zheda Rd Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9781728157306

Consensus-based economic dispatch problem (EDP) is an important distributed optimization problem in power monitoring system, which aims to minimize the total generation cost by controlling local generation units in a distributed way. Due to the vulnerability of distributed algorithm, designing resilient algorithms to ensure the normal operation of power monitoring system under cyber-attacks is of both theoretical merits and practical values. Most existing works are confined to undirected power networks and require the assumption that the tolerable number of attacks is known to all unattacked nodes. In this paper, we relax this assumption and investigate the problem for more general directed networks. A greedy algorithm is designed to obtain the directed connected dominating set as the secure area of network. Then, a resilient consensus-based economic dispatch (RCED) algorithm is proposed to ensure the solvability of EDP under adversarial attacks. Consequently, all the attacked nodes are detected, and the remaining unattacked nodes can reach the optimal solution under a new EDP, in which the incremental cost and total generation cost are decreased. Comprehensive theoretical analysis and simulations are provided to illustrate the effectiveness of the proposed algorithm.

关键词： Economics Greedy algorithms Simulation Conferences Reliability Security distributed algorithms

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Privacy-Preserving distributed Averaging via Homomorphically Encrypted Ratio Consensus

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第9期65卷 3887-3894页

作者： Hadjicostis, Christoforos N. Dominguez-Garcia, Alejandro D. Univ Cyprus Dept Elect & Comp Engn CY-1678 Nicosia Cyprus Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA

In this article, we develop distributed iterative algorithms that enable the components of a multicomponent system, each with some integer initial value, to asymptotically compute the average of their initial values, without having to reveal to other components the specific value they contribute to the average calculation. We assume a communication topology captured by an arbitrary strongly connected digraph, in which certain nodes (components) might be curious but not malicious (i.e., they execute the distributed protocol correctly, but try to identify the initial values of other nodes). We first develop a variation of the so-called ratio consensus algorithm that operates exclusively on integer values and can be used by the nodes to asymptotically obtain the average of their initial (integer) values, by taking the ratio of two integer values they maintain and iteratively update. Assuming the presence of a trusted node (i.e., a node that is not curious and can be trusted to set up a cryptosystem and not reveal any decrypted values of messages it receives), we describe how this algorithm can be adjusted using homomorphic encryption to allow the nodes to obtain the average of their initial values while ensuring their privacy (i.e., without having to reveal their initial value). We also extend the algorithm to handle situations where multiple nodes set up cryptosystems and privacy is preserved as long as one of these nodes can be trusted (i.e., the ratio of trusted nodes over the nodes that set up cryptosystems decreases).

关键词： Average consensus distributed algorithms homomorphic encryption privacy preservation

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distributed generalized Nash equilibrium seeking algorithm for nonsmooth aggregative games

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AUTOMATICA 2021年 132卷 109794-109794页

作者： Deng, Zhenhua Cent South Univ Sch Automat Changsha 410075 Peoples R China

In this paper, nonsmooth aggregative games are investigated, where the nondifferentiable cost function of every player depends on its own decision as well as the aggregate of the decisions of all players. Moreover, in the problem, the local constraints of players are general and heterogeneous convex sets, and the decisions of players are coupled by linear constraints. For purpose of distributed seeking of the variational generalized Nash equilibrium (GNE) of the game, a distributed algorithm is developed for players. In the algorithm, the dynamic average consensus is used for the estimation of the aggregate of decisions to obtain the approximation of subgradients of cost functions. Besides, the convergence of the algorithm to the variational GNE is analyzed. Finally, simulation examples illustrate the algorithm. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Aggregative games Generalized Nash equilibrium distributed algorithms Nonsmooth analysis

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Arbitrary Pattern Formation on Infinite Regular Tessellation Graphs 21

Arbitrary Pattern Formation on Infinite Regular Tessellation...

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22nd International Conference on distributed Computing and Networking (ICDCN)

作者： Cicerone, Serafino Di Fonso, Alessia Di Stefano, Gabriele Navarra, Alfredo Univ Aquila Dip Ingn & Sci Informaz & Matemat Laquila Italy Univ Perugia Dip Matemat & Informat Perugia Italy

ISBN: (纸本)9781450389334

Given a set R of robots, each one located at a different vertex of an infinite regular tessellation graph, we aim to explore the Arbitrary Pattern Formation (APF) problem. Given a multiset F of grid vertices such that vertical bar R vertical bar = vertical bar F vertical bar, APF asks for a distributed algorithm that moves robots so as to reach a configuration similar to F. Similarity means that robots must be disposed as F regardless of translations, rotations, reflections. So far, as possible discretization of the Euclidean plane only the standard square grid has been considered in the context of the classical Look-Compute-Move model. However, it is natural to consider the other regular tessellation graphs, that are triangular and hexagonal grids. For any regular tessellation graph, we provide a resolution algorithm for APF when the initial configuration is asymmetric.

关键词： distributed algorithms Mobile and collaborative systems Asynchrony Self-organization Pattern formation Graphs

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