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Phase transition of the 3-majority opinion dynamics with noisy interactions

THEORETICAL COMPUTER SCIENCE 2025年 1028卷

作者： d'Amore, Francesco Ziccardi, Isabella Bocconi Univ BIDSA Milan Italy

Communication noise is a common feature in several real-world scenarios where systems of agents need to communicate in order to pursue some collective task. Indeed, many biologically inspired systems that try to achieve agreements on some opinion must implement resilient dynamics, i.e. that are not strongly affected by noisy communications. In this work, we study the 3-MAJORITY dynamics, an opinion dynamics that has been shown to be an efficient protocol for the majority consensus problem, in which we introduce a simple feature of uniform communication noise, following D'Amore et al. (2022). We prove that, in the fully connected communication network of n agents and in the binary opinion case, the process induced by the 3-MAJORITY dynamics exhibits a phase transition. For a noise probability p < 1/3, the dynamics reach in logarithmic time an almost-consensus metastable phase which lasts for a polynomial number of rounds with high probability. We characterize this phase by showing that there exists an attractive equilibrium value s eq is an element of [n] for the bias of the system, i.e. the difference between the majority community size and the minority one. Moreover, we show that t he agreement opinion is the initial majority one root if the bias towards it is of magnitude Omega(root nlog n) in the initial configuration. If, instead, p > 1/3, we show that no form of consensus is possible, and any information regarding the initial majority opinion is lost in logarithmic time with high probability. Despite more communications per-round being allowed, the 3-MAJORITY dynamics surprisingly turns out to be less resilient to noise than the UNDECIDED-STATE dynamics, whose noise threshold value is p = 1/2.

关键词： Opinion dynamics Consensus problem Randomized algorithms distributed computing

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A Novel Labeling Method for Radar Pulse Data With Complex Modulations and Parametric Overlaps

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IEEE ACCESS 2025年 13卷 58978-58991页

作者： Wei, Youtan Shen, Yang Southwest Inst Elect Equipment Chengdu 610036 Peoples R China

This paper aims to solve a problem: using azimuthal information to label radar pulse data with complex inter-pulse modulations and overlaps on electromagnetic parameters. In these tough conditions, azimuthal information is the key for data labeling instead of parameter-based methods. According to the measurement principle, the azimuthal information from the identical emitter shares the same positional relationship between the radiation source and the receivers. We propose a transformation, the inv-Hough transform, to reveal this positional relationship. The data from the identical emitter show highly linearly correlated distributions in the transformed space. Through transformation, the problem becomes a clustering problem. Dirichlet Process Mixture Models (DPMM) are widely used to address clustering problems, as they can automatically estimate the number of clusters. However, the outliers deteriorate clustering performances. Therefore, two improvements based on DPMM are proposed: adding a pre-clustering step to improve the outlier adaptation and distributed inference to improve the federated learning capability. The experimental results show that our method outperforms parameter-based methods when complex interpulse modulations and overlaps on electromagnetic parameters occur. In addition, the pre-clustering step significantly improves outlier adaptation and parametric stability for our method. And the method can work in federated learning scenarios.

关键词： Labeling Radar Receivers Transforms Sorting Radar signal processing Federated learning Reconnaissance Mixture models Electromagnetics Azimuth distributed computing Dirichlet process mixture models federated learning Hough transform labeling pulse descriptive word radar signal processing

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MapReduce-Enhanced Fuzzy K-Least Medians for Qualitative Clustering of Document Big Data

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2025年第4-5期37卷

作者： Sardar, Tanvir Habib Ansari, Zahid Ahmed Theerthagiri, Prasannavenkatesan Karthikeyan, P. Ayyasamy, Vadivel Saini, Dilip Kumar Jang Bahadur Dayananda Sagar Univ Sch Engn Dept Comp Sci & Engn Bengaluru India ALIGARH MUSLIM UNIV UNIV POLYTECH EES ALIGARH India GITAM Univ GITAM Sch Technol Comp Sci & Engn Bengaluru India RV Univ Dept Comp Sci & Engn Bangalore India GITAM Univ Gitam Sch Technol Dept Artificial Intelligence & Data Sci Bengaluru India Dayananda Sagar Univ Sch Engn Dept Comp Sci & Engn Cyber Secur Bengaluru India

Researchers design MapReduce-enhanced versions of traditional clustering algorithms to obtain the time performance benefit in big data clustering. However, the current literature shows that only a handful of partitioning clustering algorithms are enhanced using the MapReduce model. This work proposes a MapReduce-enhanced design of the Fuzzy K-Least Medians algorithm (MRFK-LstMdns) and its two novel variations. The purpose is to determine the best-performing MapReduce-enhanced partitioning clustering algorithms among the proposed and existing ones in terms of time performance and cluster quality. The work first preprocesses different self-crawled document datasets. Then, an optimal noise removal process is employed to make the dataset optimally noise-free. The proposed MRFK-LstMdns and its two novel variations are designed using three MapReduce job chaining. Each of the jobs performs the staged and suitable algorithmic parts. The proposed algorithms' time performance and cluster quality are compared against the existing MapReduce-enhanced partitioning algorithms. Although the proposed algorithm's time complexity is higher than that of existing algorithms as KLeast median is well known to execute in more time than other algorithms such as KMeans, KMedoids and its fuzzy versions, its efficient design, incorporating a chained MapReduce job execution, ensures that the actual execution time remains comparable to that of the existing algorithms. A majority voting technique using seven cluster quality measures shows that the MRFK-LstMdns generates better quality clusters than existing algorithms.

关键词： cluster analysis distributed computing document big data Hadoop machine learning MapReduce

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Automating distributed In-network Classification with Runtime Programmability

Automating Distributed In-network Classification with Runtim...

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2024 CoNEXT Student Workshop

作者： Zhu, Di Kim, Hyojoon Univ Virginia Charlottesville VA 22903 USA

This paper presents ACORN, a distributed system for in-network machine-learning classification applications. ACORN automatically translates user-level Python ML programs into network-level programs and deploys them wi... 详细信息

ISBN: (纸本)9798400712555

关键词： distributed computing in-network computing machine learning

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TACC: Topology-Aware Coded computing for distributed Graph Processing

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2020年 6卷 508-525页

作者： Guler, Basak Avestimehr, A. Salman Ortega, Antonio Univ Southern Calif Dept Elect & Comp Engn Los Angeles CA 90089 USA

This article proposes a coded distributed graph processing framework to alleviate the communication bottleneck in large-scale distributed graph processing. In particular, we propose a topology-aware coded computing (TACC) algorithm that has two novel salient features: (i) a topology-aware graph allocation strategy, and (ii) a coded aggregation scheme that combines the intermediate computations for graph processing while constructing coded messages. The proposed setup results in a trade-off between computation and communication, in that increasing the computation load at the distributed parties can in turn reduce the communication load. We demonstrate the effectiveness of the TACC algorithm by comparing the communication load with existing setups on both Erdos-Renyi and Barabasi-Albert type random graphs, as well as real-world Google web graph for PageRank computations. In particular, we show that the proposed coding strategy can lead to up to 82% reduction in communication load and up to 46% reduction in overall execution time, when compared to the state-of-the-art and implemented on the Amazon EC2 cloud compute platform.

关键词： distributed computing large-scale graph processing graph signal filtering

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Scalable and accurate online multivariate anomaly detection

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INFORMATION SYSTEMS 2025年 131卷

作者： Salles, Rebecca Lange, Benoit Akbarinia, Reza Masseglia, Florent Ogasawara, Eduardo Pacitti, Esther Natl Inst Res Digital Sci & Technol INRIA Montpellier France Univ Montpellier Montpellier France Fed Ctr Technol Educ Rio de Janeiro CEFET RJ Rio De Janeiro Brazil

The continuous monitoring of dynamic processes generates vast amounts of streaming multivariate time series data. Detecting anomalies within them is crucial for real-time identification of significant events, such as environmental phenomena, security breaches, or system failures, which can critically impact sensitive applications. Despite significant advances in univariate time series anomaly detection, scalable and efficient solutions for online detection in multivariate streams remain underexplored. This challenge becomes increasingly prominent with the growing volume and complexity of multivariate time series data in streaming scenarios. In this paper, we provide the first structured survey primarily focused on scalable and online anomaly detection techniques for multivariate time series, offering a comprehensive taxonomy. Additionally, we introduce the Online distributed Outlier Detection (2OD) methodology, a novel well-defined and repeatable process designed to benchmark the online and distributed execution of anomaly detection methods. Experimental results with both synthetic and real-world datasets, covering up to hundreds of millions of observations, demonstrate that a distributed approach can enable centralized algorithms to achieve significant computational efficiency gains, averaging tens and reaching up to hundreds in speedup, without compromising detection accuracy.

关键词： Multivariate Time series Online Anomaly detection Scalable Accurate detection distributed computing

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High-Fidelity High-Resolution Regional Seismic Risk and Resilience Assessment of Large Building Inventories

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EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS 2025年第5期54卷 1376-1396页

作者： Dahal, Laxman Burton, Henry Zhong, Kuanshi Univ Calif Los Angeles Dept Civil & Environm Engn Los Angeles CA 90095 USA Univ Cincinnati Dept Civil & Architectural Engn & Construct Manage Los Angeles OH USA

Historically, building design codes have been governed by the life-safety performance standard with minimal emphasis on other metrics such as economic loss and downtime. However, recent advancements in earthquake engineering have put in motion a national-level initiative to explicitly codify functional recovery as a target performance objective. While the importance of this effort has been broadly acknowledged by engineers and policymakers, there is a need to develop efficient computational strategies for evaluating the performance and cost-benefit implications of these resilience-based standards at the regional scale. It is with this vision in mind that this paper makes two primary contributions. First, an efficient computational framework for performing high-fidelity high-resolution seismic risk and resilience assessments of large building inventories is proposed. The framework harnesses automation, optimization, and distributed computing to render high fidelity and resolution risk-based seismic assessments computationally feasible. Second, a new approach to performing stochastic event set-based evaluation building portfolios is proposed, which utilizes building-specific performance-based assessments that include site-specific ground motion selection. A case study is presented on a risk-based seismic loss and functional recovery evaluation of more than 15,000 residential woodframe buildings in the City of Los Angeles. By leveraging automation, optimization, and high-performance computing, we are able to reduce the runtime for such an assessment from months to days.

关键词： automation distributed computing high-fidelity high-resolution regional assessment resilience seismic risk

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Blockchain-Based Federated Learning Methodologies in Smart Environments for Drone Technology

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2025年

作者： Chalapathi, Mukkoti Maruthi Venkata Sreenivasulu, K. Jeya, R. Faheem, Muhammad Mohana, R. Madana Khan, Arfat Ahmad Ramana, Kadiyala VIT AP Univ Sch Comp Sci & Engn Amaravati Andhra Pradesh India Anantha Lakshmi Inst Technol & Sci Dept Comp Sci & Engn Anantapuramu Andhra Pradesh India SRM Inst Sci & Technol Sch Comp Dept Comp Technol Chennai 603203 Tamil Nadu India VTT Tech Res Ctr Finland Espoo 04150 Finland Stanley Coll Engn & Technol Women Dept Comp Sci & Engn Hyderabad 500001 Telangana India Khon Kaen Univ Coll Comp Dept Comp Sci Khon Kaen 40002 Thailand Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad 500075 Telangana India

High-security transactions are stored in a chain of blocks using blockchain technology. Security and privacy concerns may be addressed by using blockchain technology. Federated learning is a paradigm for increasing data mining accuracy and precision by ensuring data privacy and security for both internet of things (IoT) devices and users in smart environments. Algorithms for dealing with limited training data and avoiding a particular model are included in the proposed model. Drones are indeed being researched and proactively employed in emergency situations, as well as catastrophic and high-casualty situations. Governance, security, flying circumstances, security and privacy, authorization, confidentiality, and specifics around the creation, maintenance, and operation of a medical drone network are now obstacles to extending their usage in emergency medicine and emergency medical service (EMS). In this paper, we present the more effective FL to protect the data privacy of drones, which involves doing local and global parameter updates for drones and exchanging training parameters concerning fog nodes, rather than sending drone raw data to the cloud. Even so, eavesdropping and analyzing parameters that are uploaded during the training procedure might still provide ground eavesdroppers with information on drone privacy and operations. Specifically, in this work, we examine how to optimize the power management strategies to optimize all the required parameters of FL security cost while being bound by battery usage of drone capacity and the necessity for quality of service (QoS) (i.e., required training time). Extensive simulations were conducted, and the results demonstrate that the proposed Secure Federated Power Control (SFPC) can effectively improve utilities for drones, promote high-quality model sharing, and ensure privacy protection in federated learning, compared with existing schemes. (c) 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LL

关键词： distributed computing sustainable society blockchain federated learning internet of things

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A model for distributed in-network and near-edge computing with heterogeneous hardware

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 105卷 395-409页

作者： Cooke, Ryan A. Fahmy, Suhaib A. Univ Warwick Sch Engn Coventry W Midlands England

Applications that involve analysis of data from distributed networked data sources typically involve computation performed centrally in a datacenter or cloud environment, with some minor preprocessing potentially performed at the data sources. As these applications grow in scale, this centralized approach leads to potentially impractical bandwidth requirements and computational latencies. This has led to interest in edge computing, where processing is moved nearer to the data sources, and recently, in-network computing, where processing is done as data progresses through the network. This paper presents a model for reasoning about distributed computing at the edge and in the network, with support for heterogeneous hardware and alternative software and hardware accelerator implementations. Unlike previous distributed computing models, it considers the cost of computation for compute-intensive applications, supports a variety of hardware platforms, and considers a heterogeneous network. The model is flexible and easily extensible for a range of applications and scales, and considers a variety of metrics. We use the model to explore the key factors that influence where computational capability should be placed and what platforms should be considered for distributed applications. (C) 2019 Elsevier B.V. All rights reserved.

关键词： distributed computing Edge computing In-network computing Hardware acceleration

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A Web-based Platform for Publication and distributed Execution of computing Applications 14

A Web-based Platform for Publication and Distributed Executi...

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IEEE 14th International Symposium on Parallel and distributed computing (ISPDC)

作者： Sukhoroslov, Oleg Volkov, Sergey Afanasiev, Alexander Russian Acad Sci Kharkevich Inst Inst Informat Transmiss Problems Ctr Distributed Comp Moscow Russia

ISBN: (纸本)9781467371483

Researchers increasingly rely on using web-based systems for accessing and running scientific applications across distributed computing resources. However existing systems lack a number of important features, such as publication and sharing of scientific applications as online services, decoupling of applications from computing resources and providing remote programmatic access. This paper presents Everest, a web-based platform for researchers supporting publication, execution and composition of applications running across distributed computing resources. Everest addresses the described challenges by relying on modern web technologies and cloud computing models. It follows the Platform as a Service (PaaS) cloud delivery model by providing all its functionality via remote web and programming interfaces. Any application added to Everest is automatically published both as a user-facing web form and a web service. Another distinct feature of Everest is the ability to attach external computing resources by any user and flexibly use these resources for running applications. The paper provides an overview of the platform's architecture and its main components, describes recent developments, presents results of experimental evaluation of the platform and discusses remaining challenges.

关键词： distributed computing web-based platform web services REST API software as a service platform as a service resource integration application composition

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