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Privacy-Preserving distributed Economic Dispatch of Microgrids Over Directed Networks via State Decomposition: A Fast Consensus Algorithm

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第3期20卷 4092-4102页

作者： Chen, Wei Wang, Zidong Dong, Hongli Mao, Jingfeng Liu, Guo-Ping Southern Univ Sci & Technol Shenzhen Key Lab Control Theory & Intelligent Syst Shenzhen 518055 Peoples R China Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH England Northeast Petr Univ Sanya Offshore Oil & Gas Res Inst Sanya 572024 Peoples R China Northeast Petr Univ Artificial Intelligence Energy Res Inst Daqing 163318 Peoples R China Northeast Petr Univ Heilongjiang Prov Key Lab Networking & Intelligent Daqing 163318 Peoples R China Nantong Univ Sch Elect Engn Nantong 226019 Peoples R China

This article is concerned with the privacy-preserving distributed economic dispatch problem of microgrids. The main goal of this work is to develop a privacy-preserving distributed optimization algorithm over directed networks, aiming to achieve supply-demand balance at the lowest economic cost under practical constraints while preventing the leakage of power-sensitive information. For this purpose, a distributed optimization algorithm with a constant step size is proposed by combining the decentralized exact first-order algorithm with the push-sum protocol, which offers an advantage in terms of fast convergence. In addition, to ensure privacy preservation, a state-decomposition approach is employed by randomly dividing the state into two parts, where only partial state information is transmitted. Moreover, the effectiveness of the privacy-preserving scheme against honest-but-curious nodes and external eavesdroppers is demonstrated through rigorous analysis. Finally, simulation studies demonstrate the validity and superiority of the developed privacy-preserving distributed algorithm.

关键词： Privacy Optimization Microgrids Convergence Costs Directed graphs distributed algorithms Consensus-based optimization algorithm economic dispatch (ED) microgrids privacy preservation push-sum protocol state decomposition

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distributed Constrained Optimization for Second-Order Multiagent Systems via Event-Based Communication

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第9期54卷 5317-5326页

作者： Huang, Yi Meng, Ziyang Sun, Jian Beijing Inst Technol Sch Automat State Key Lab Autonomous Intelligent Unmanned Sys Beijing 100081 Peoples R China Tsinghua Univ Dept Precis Instrument Beijing 100084 Peoples R China

This article studies the distributed constrained optimization problems for the discrete-time second-order multiagent systems (MASs), in which each agent privately owns local cost function and nonidentical convex set constraints. To solve this problem, a projection-based distributed event-triggered algorithm is developed via the constant step-sizes, which achieves an ergodic convergence rate O(1/k) for the general convex functions. By applying the event-triggered mechanism, the proposed algorithm can avoid unnecessary communication among the agents. Moreover, it is shown that the introduced event-triggered component does not sacrifice the convergence rate. Finally, a simulation example is carried out to demonstrate the theoretical results.

关键词： Optimization Heuristic algorithms Convergence distributed algorithms Multi-agent systems Machine learning algorithms Costs distributed optimization event-triggered communication multiagent systems (MASs) set constraints second-order dynamics

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Proving distributed algorithms for Mobile Agents: Examples of Spanning Tree Computation in Dynamic Networks

Proving Distributed Algorithms for Mobile Agents: Examples o...

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IEEE/ACS International Conference on Computer Systems and Applications

作者： Mouna Ktari Mohamed Amine Haddar Ahmed Hadj Kacem Mohamed Mosbah ReDCAD Laboratory University of Sfax FSEGS 3018 Sfax Tunisia Information technology department Taif University Saoudi Arabia LaBRI Laboratory University of Bordeaux F-33405 Talence France

ISBN: (纸本)9781509004799

In a dynamic network topological events can occur at any time, and no stable periods can be assumed. To make designing distributed algorithms easier, we model these latter with a local computation model. The implementation of a local computation model using message passing communication model has given rise to various problems. Among these we can mention the use of a great amount of communication and computation resources. In order to solve these problems, we propose another implementation of rewriting systems using mobile agents. We present then, using local computations, a framework for describing distributed algorithms for mobile agents in a dynamic network. We make use of the high level encoding of these algorithms as transition rules. The main advantage of this uniform and formal approach is the proof correctness of distributed algorithms. We illustrate this approach by giving an example of distributed computation of a hierarchical spanning tree by mobile agents in a dynamic network.

关键词： Proving distributed algorithms Spanning Tree Computation Dynamic Networks dynamic network distributed algorithms Mobile agents Computational modeling Spanning tree rewriting systems arithmetic

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distributed Weakly Convex Optimization Under Random Time-Delay Interference

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第1期11卷 212-224页

作者： Wei, Mengli Yu, Wenwu Liu, Hongzhe Xu, Qian Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Sch Math Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China State Grid Zhejiang Econ Res Inst Hangzhou 310053 Peoples R China

In this article, a special class of distributed stochastic nonconvex optimization problem is investigated. Each agent in the network only has access to a local stochastic weakly convex objective function and can only communicate with its neighbours in a random time-delay environment. For solving the considered problem with the distributed communication under time-delay interference, the distributed weakly convex delay-tolerance algorithm (DWDTA) with diminishing step-size and fixed step-size are proposed, respectively. Specifically, we show the convergence of the DWDTA with diminishing step-size by using Moreau Envelope measurement and demonstrate the linear convergence of the DWDTA with fixed step-size under sharpness condition. Our convergence results explicitly characterize the influences of the weakly convex function and the random time-delay interference on the convergence performances, respectively. Finally, numerical results are worked out to verify the effectiveness of the DWDTA.

关键词： Convex functions Convergence Optimization Interference Delays Linear programming distributed algorithms distributed weakly convex optimization delay tolerant linear convergence fixed step-size

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distributed Control Design for Solving Linear Algebraic Equations via Adjustable Domains

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第12期69卷 8790-8797页

作者： Li, Juntao Liang, Cong Meng, Deyuan Henan Normal Univ Coll Math & Informat Sci Xinxiang 453007 Peoples R China Beihang Univ BUAA Res Div 7 Beijing 100191 Peoples R China Beihang Univ BUAA Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

This article aims to develop a general and designable distributed algorithm for solving linear algebraic equations (LAEs), which departs from the design framework based on orthogonal projection. The concept of adjustable domains for the parameter matrix is introduced, enabling the algorithm to derive flexible and variable updating rules for agents. By leveraging adjustable domains in control design, all agents can exponentially converge to a common (least squares) solution of (un)solvable LAEs under arbitrary initialization conditions, regardless of whether the LAEs admit a unique solution or multiple solutions. Moreover, two novel distributed algorithms for obtaining the least squares solution are proposed within both row and column partitioning frameworks. A simulation example is provided to demonstrate the effectiveness of the proposed distributed algorithms.

关键词： distributed algorithms Mathematical models Task analysis Partitioning algorithms Topology Symmetric matrices Matrix decomposition Adjustable domain distributed algorithm linear algebraic equations (LAEs) multiagent system time-varying topology

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Graph Neural Network for distributed Beamforming and Power Control in Massive URLLC Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第8期23卷 9099-9112页

作者： Gu, Yifan She, Changyang Bi, Suzhi Quan, Zhi Vucetic, Branka Shenzhen Univ State Key Lab Radio Frequency Heterogeneous Integr Shenzhen 518060 Guangdong Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Guangdong Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

In this paper, we consider a massive ultrareliable and low-latency communication (mURLLC) network with multiple antennas at each transmitter. We formulate a distributed beamforming and power control problem by minimizing the logarithm-based average utility of decoding error probability for the worst link over different network topologies and channels, where the policy is represented by a graph neural network (GNN). To reduce signaling overhead and computation delay for distributed inference, we first develop a GNN for mURLLC (G4U) framework, where the graph embedding of each node is updated according to its previous graph embedding. In addition, we represent the local message of each node by the amplitude and phase of its pilot signal, such that the graph convolution can be accomplished efficiently by broadcasting the pilot signals. To further reduce the overall latency, we propose the pipeline G4U (PG4U), where each node determines its policy solely based on the channel state information acquired in the previous frames. The feedforward neural networks in PG4U for graph convolution can be executed efficiently during data transmission. To train the GNNs in mURLLC where the decoding error probability is small, we develop a novel loss function based on the asymptotic expression of the Gaussian $Q$ -function. Simulation results show that G4U and PG4U are scalable to a different number of links. They can outperform the existing GNN and other policies significantly in terms of the QoS outage probability. Moreover, PG4U is suitable for mURLLC networks with short frame durations and highly correlated channels, while G4U is suitable for moderate frame durations with low channel correlation coefficients.

关键词： Graph neural network beamforming power control ultrareliable and low-latency communication distributed algorithms Graph neural network beamforming power control ultrareliable and low-latency communication distributed algorithms

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Trustworthy distributed Average Consensus Based on Locally Assessed Trust Evaluations

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第1期70卷 371-386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

This article proposes a distributed algorithm for average consensus in a multiagent system under a fixed bidirectional communication topology, in the presence of untrustworthy (malicious) agents (nodes) that may try to influence the average consensus outcome by manipulating their updates. The proposed algorithm converges asymptotically to the average of the initial values of the trustworthy (nonmalicious) nodes, which we refer to as the trustworthy average, as long as the underlying topology that describes the information exchange among the trustworthy nodes is connected. We first present a distributed iterative algorithm that assumes that each node receives (at each iteration or periodically) side information about the trustworthiness of the other nodes, and it uses such trust assessments to determine whether or not to incorporate messages received from its neighbors, as well as to make proper adjustments in its calculation depending on whether a previously trustworthy neighbor becomes untrustworthy or vice-versa. We show that, as long as the trust assessments for each trustworthy node eventually reflect correctly the status (trustworthy or untrustworthy) of its neighboring nodes, the algorithm guarantees asymptotic convergence to the trustworthy average. We subsequently discuss how the proposed algorithm can be enhanced with functionality that enables each node to obtain trust assessments about its neighbors by utilizing information that it receives from its two-hop neighbors at infrequent, perhaps randomly chosen, time instants.

关键词： Topology Multi-agent systems distributed algorithms Convergence Bidirectional control Stochastic processes Heuristic algorithms distributed averaging distributed trust assessment fault-tolerant consensus resilience multiagent systems trustworthy computation

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A Novel Single-Time Scale distributed Attack-Resilient Algorithm for Multi-Cluster Games

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第5期11卷 4252-4263页

作者： Li, Fan Liu, Dongxin Nian, Xiaohong Cent South Univ Key Lab Inst Cluster Unmanned Syst Sch Automat Changsha 410083 Peoples R China

In this paper, the multi-cluster game with Denial-of-Service (DoS) attacks is first investigated, which cannot be solved by existing distributed algorithms due to the presence of DoS attacks that can disrupt the connectivity of the network. Specifically, each cluster consists of some agents that aim to collaboratively minimize the sum of their local cost functions depending on the decisions of all the game participants. The clusters are competitive with each other, and each cluster is considered to be a virtual player aiming to find its own Nash equilibrium (NE). A new single-time scale distributed attack-resilient algorithm requiring only partial-decision information is designed. With the help of the hybrid system method and Lyapunov stability analysis, rigorous analysis is performed to demonstrate that the algorithm achieves exponential convergence under certain bounded conditions on the frequency and duration of attacks. Finally, the numerical simulation is given to corroborate the effectiveness of the algorithm.

关键词： distributed algorithms DoS attacks multi-agent systems multi-cluster games nash equilibrium (NE) DoS attacks multi-agent systems multi-cluster games nash equilibrium (NE)

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A distributed Augmenting Path Approach for the Bottleneck Assignment Problem

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第2期69卷 1210-1217页

作者： Khoo, Mitchell Wood, Tony A. Manzie, Chris Shames, Iman Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia Ecole Polytech Fed Lausanne EPFL Sycamore CH-1015 Lausanne Switzerland Australia Natl Univ Sch Engn CIICADA Lab Canberra ACT 0200 Australia

We develop an algorithm to solve the bottleneck assignment problem (BAP) that is amenable to having computation distributed over a network of agents. This consists of exploring how each component of the algorithm can be distributed, with a focus on one component in particular, i.e., the function to search for an augmenting path. An augmenting path is a common tool used in most BAP algorithms and poses a particular challenge for this distributed approach. Given this significance, we compare the properties of two different methods to search for an augmenting path in a bipartite graph. We evaluate the derived approaches with a simulation-based complexity investigation.

关键词： Autonomous agents autonomous systems distributed algorithms multi-agent systems

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Scalable distributed Optimization of Multi-Dimensional Functions Despite Byzantine Adversaries

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2024年 10卷 360-375页

作者： Kuwaranancharoen, Kananart Xin, Lei Sundaram, Shreyas Purdue Univ W Lafayette IN 47907 USA Intel Corp Hillsboro OR 97124 USA Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA

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 $F$ (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.

关键词： Byzantine attacks convex optimization distributed algorithms fault tolerance graph theory machine learning multi-agent systems network security

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