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Covariance Intersection-Based Kalman Consensus Filtering With Sequential Replay Attack Detection Over Sensor Networks

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IEEE SENSORS JOURNAL 2024年第15期24卷 24880-24891页

作者： Ma, Yinping Li, Yinya Wang, Ning Liang, Yuan Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Nanjing Inst Technol Sch Automot & Rail Transit Nanjing 211167 Peoples R China

This article focuses on the security issue of consensus-based distributed filtering under replay attacks over wireless sensor networks. The local information from each sensor, which could be subject to replay attacks, is sent to its neighboring nodes through wireless communication channels. However, the existing strategies of replay attack detection cannot be migrated into the scenario where local estimates and error covariances are simultaneously transmitted. As for this concern, a new replay attack detection strategy is put forward to verify the received local estimate and error covariance for distributed filters' consensus on information, respectively. Afterward, on the basis of the aforementioned strategy, a covariance intersection (CI)-based fusion algorithm is developed to fuse the local information sequentially. This algorithm guarantees the estimate consistency in the presence of replay attacks and unknown cross-correlations. Simultaneously, the stability properties of the proposed fusion algorithm with sequential attack detection strategy are analyzed. Finally, the effectiveness of the theoretical results is validated by two examples.

关键词： Sensors Estimation Detectors Wireless sensor networks State estimation Kalman filters distributed databases Keywords Covariance intersection (CI) Kalman consensus filtering replay attack sequential detection stability properties

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CB-DSL: Communication-Efficient and Byzantine-Robust distributed Swarm Learning on Non-i.i.d. Data

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2024年第1期10卷 322-334页

作者： Fan, Xin Wang, Yue Huo, Yan Tian, Zhi Beijing Forestry Univ Sch Informat Sci & Technol Beijing 100083 Peoples R China Georgia State Univ Dept Comp Sci Atlanta 30303 GA USA Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China George Mason Univ Dept Elect & Comp Engn Fairfax VA 22030 USA

The valuable data collected by IoT devices together with the resurgence of machine learning (ML) stimulate the latest trend of artificial intelligence (AI) at the edge. However, traditional ML and recent federated learning (FL) face major challenges including communication bottleneck, data heterogeneity, and security concerns in edge IoT. Meanwhile, the swarm nature of IoT systems is overlooked by most existing literature, which calls for new designs of distributed learning algorithms. Inspired by the success of biological intelligence (BI) of gregarious organisms, we propose a novel edge learning approach for swarm IoT, called communication-efficient and Byzantine-robust distributed swarm learning (CB-DSL), through a holistic integration of AI-enabled stochastic gradient descent and BI-enabled particle swarm optimization. To deal with the non-i.i.d. data issues and Byzantine attacks, a small amount of global data samples are introduced in CB-DSL and shared among IoT workers, which alleviates the local data heterogeneity effectively and enables to fully utilize the exploration-exploitation mechanism of swarm intelligence. Our convergence analysis theoretically demonstrates that the CB-DSL is superior to the standard FL with better convergence behavior. We also evaluate the model divergence of CB-DSL by deriving its upper bound, which measures the effectiveness of the introduction of the globally shared dataset.

关键词： Internet of Things distributed databases Convergence Particle swarm optimization Distance learning Computer aided instruction Data models distributed swarm learning federated learning particle swarm optimization non-i.i.d. data convergence analysis model divergence analysis

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Locality-Aware and Fault-Tolerant Batching for Machine Learning on distributed Datasets

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2024年第2期12卷 370-387页

作者： Liu, Liu Ding, Zhijun Cheng, Dazhao Zhou, Xiaobo Tongji Univ Dept Comp Sci & Technol Shanghai 201804 Peoples R China Univ Macau IOTSC Lab Macau Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Hubei Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China

The performance of distributed ML training is largely determined by workers that generate gradients in the slowest pace, i.e., stragglers. The state-of-the-art load balancing approaches consider that each worker stores a complete dataset locally and the data fetching time can be ignored. They only consider the computation capacity of workers in equalizing the gradient computation time. However, we find that in scenarios of ML on distributed datasets, whether in edge computing or distributed data cache systems, the data fetching time is non-negligible and often becomes the primary cause of stragglers. In this paper, we present LOFT, an adaptive load balancing approach for ML upon distributed datasets at the edge. It aims to balance the time to generate gradients at each worker while ensuring the model accuracy. Specifically, LOFT features a locality-aware batching. It builds performance and optimization models upon data fetching and gradient computation time. Leveraging the models, it develops an adaptive scheme based on grid search. Furthermore, it offers Byzantine gradient aggregation upon Ring All-Reduce, which makes itself fault-tolerant under Byzantine gradients brought by a small batch size. Experiments with twelve public DNN models and four open datasets show that LOFT reduces the training time by up to 46%, while reducing the training loss by up to 67% compared to LB-BSP.

关键词： Training distributed databases Computational modeling Adaptation models Data models Graphics processing units Load management Byzantine gradient distributed dataset machine learning training straggler

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A New Data Completion Perspective on Sparse CrowdSensing: Spatiotemporal Evolutionary Inference Approach

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第3期24卷 1357-1371页

作者： Wang, En Song, Zixuan Wu, Mengni Liu, Wenbin Yang, Bo Yang, Yongjian Wu, Jie Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia 19122 PA USA

Mobile CrowdSensing (MCS) has emerged as a popular paradigm to engage mobile users in collaborative sensing tasks. However, its performance is hindered by its limited spatiotemporal range and the cost of data collection. An effective strategy is to integrate Sparse MCS with data completion, allowing for unsensed data inference. However, when confronted with situations where sensed data is excessively sparse, data inference results may be unsatisfactory due to several challenges including: 1) uneven data distribution, 2) complex spatiotemporal correlation, and 3) the presence of inference noise. To address these challenges, we propose a model named Spatiotemporal Evolutionary Inference (STEI) that achieves accurate inference of unsensed data in Sparse MCS. Specifically, we complete the unsensed data by uncovering strong local correlations in the data and gradually evolving those correlations to the global situation. In each evolution step, we thoroughly consider the impact of spatiotemporal consistency and difference. To minimize the interference of noise during the evolution process, we design an adaptive coefficient to enhance the dependence on sensed data. Finally, to validate the effectiveness of STEI, we conduct extensive qualitative and quantitative experiments using three popular datasets. The experimental results demonstrate that our approach excels in accurately inferring data, particularly in situations where the distribution of data is notably uneven.

关键词： Correlation Spatiotemporal phenomena Sensors Sparse matrices Mobile computing distributed databases Accuracy Matrix decomposition Crowdsensing Feature extraction Data completion sparse mobile CrowdSensing spatiotemporal correlation spatiotemporal data inference

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distributed Stochastic Projection-Free Algorithm for Constrained Optimization

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2479-2494页

作者： Jiang, Xia Zeng, Xianlin Xie, Lihua Sun, Jian Chen, Jie Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong 999077 Peoples R China Beijing Inst Technol Sch Automat Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China

This article proposes a distributed stochastic projection-free algorithm for large-scale constrained finite-sum optimization whose constraint set is complicated such that the projection onto the constraint set can be expensive. The global cost function is allocated to multiple agents, each of which computes its local stochastic gradients and communicates with its neighbors to solve the global problem. Stochastic gradient methods enable low computational complexity, while they are hard and slow to converge due to the variance caused by random sampling. To construct a convergent distributed stochastic projection-free algorithm, this article incorporates variance reduction and gradient tracking techniques in the Frank-Wolfe (FW) update. We develop a novel sampling rule for the variance reduction technique to reduce the variance introduced by stochastic gradients. Complete and rigorous proofs show that the proposed distributed projection-free algorithm converges with a sublinear convergence rate and enjoys superior complexity guarantees for both convex and nonconvex objective functions. By comparative simulations, we demonstrate the convergence and computational efficiency of the proposed algorithm.

关键词： Optimization Complexity theory Convergence Stochastic processes distributed algorithms Vectors Approximation algorithms Minimization Linear programming distributed databases distributed solver finite-sum optimization Frank-Wolfe (FW) algorithm stochastic gradient variance reduction

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Minerva: Decentralized Collaborative Query Processing Over InterPlanetary File System

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IEEE TRANSACTIONS ON BIG DATA 2025年第2期11卷 669-683页

作者： Yao, Zhiyi Ding, Bowen Bai, Qianlan Xu, Yuedong Fudan Univ Sch Informat Sci & Technol Shanghai 200437 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200437 Peoples R China

Data silos create barriers to accessing and utilizing data dispersed over networks. Directly sharing data easily suffers from the long downloading time, the single point failure and the untraceable data usage. In this paper, we present Minerva, a peer- to-peer cross-cluster data query system based on the InterPlanetary File System (IPFS). Minerva makes use of the distributed Hash table (DHT) lookup to pinpoint the locations that store content chunks. We theoretically model the DHT query delay and introduce a fat Merkle tree structure as well as the DHT caching to reduce it. We design the query plan for read and write operations on top of Apache Drill that enables the collaborative query with decentralized workers. We conduct comprehensive experiments on Minerva, and the results show that Minerva achieves up to 2.08x query performance acceleration compared to the original IPFS data query, and can complete data analysis queries on the Internet-like environments within an average latency of 0.615 second. With a collaborative query, Minerva could perform up to 1.39x performance acceleration than the centralized query with raw data shipment.

关键词： distributed databases InterPlanetary File System Memory Soft sensors Big Data Structured Query Language Engines IPFS decentralized query distributed hash table Merkle tree

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Federated Gradient Matching Pursuit

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第6期70卷 4512-4537页

作者： Jeong, Halyun Needell, Deanna Qin, Jing Univ Calif Los Angeles Dept Math Los Angeles CA 90095 USA Univ Kentucky Dept Math Lexington KY 40506 USA

Traditional machine learning techniques require centralizing all training data on one server or data hub. However, with the development of communication technologies and a huge amount of decentralized data on many clients, collaborative machine learning has become the main interest while providing privacy-preserving frameworks. Federated learning (FL) provides such a solution to learn a shared model while keeping training data at local clients. On the other hand, in a wide range of machine learning and signal processing applications, the desired solution naturally has a certain structure that can be framed as sparsity with respect to a certain dictionary. This problem can be formulated as an optimization problem with sparsity constraints and solving it efficiently has been one of the primary research topics in the traditional centralized setting. In this paper, we propose a novel algorithmic framework, federated gradient matching pursuit (FedGradMP), to solve the sparsity constrained minimization problem in the FL setting. We also generalize our algorithms to accommodate various practical FL scenarios when only a subset of clients participate per round, when the local model estimation at clients could be inexact, or when the model parameters are sparse with respect to general dictionaries. Our theoretical analysis shows the linear convergence of the proposed algorithms. A variety of numerical experiments are conducted to demonstrate the great potential of the proposed framework - fast convergence both in communication rounds and computation time for many important scenarios without intricate parameter tuning.

关键词： Convergence Servers Dictionaries Matching pursuit algorithms Federated learning Tuning distributed databases sparse recovery gradient matching pursuit random algorithm

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Leveraging the Learning Curve: Reusing Existing Architectural Patterns to Design and Implement MAS

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IEEE ACCESS 2025年 13卷 45809-45825页

作者： Casals, Arthur Brandao, Anarosa A. F. Univ Sao Paulo Escola Politecn BR-05508220 Sao Paulo Brazil

Recent advancements in AI have led to the development of specialized systems related to multi-agent systems (MAS). However, the inherently collaborative nature of agents is often overlooked, and many of these specialized systems are used as components by other AI systems. From a software engineering perspective, this context can benefit from aligning the architectural characteristics of distributed systems with the inherently distributed nature of MAS. We propose that introducing a minimal set of agent-related concepts into the distributed Systems (DS) domain can improve the engineering of modern MAS by leveraging techniques from DS engineering with established agent theory. In this study, we recapitulated the common origins of MAS and DS by drawing architectural parallels to establish a unified engineering approach. We then defined a minimal set of agent concepts to perform two practical studies on leveraging MAS development. First, we incorporated these concepts into a DS architectural pattern to design a distributed MAS. We then used these concepts in a graduate course to teach MAS engineering to students with no prior knowledge of agent theory. The learning outcomes from both courses included successful MAS implementation using DS tools and techniques. Although more than two-thirds of these students had no practical experience in developing distributed systems, the average final grade in both courses was above 80%, thus validating our approach. Finally, we discuss how this study supports the development of advanced systems using modern AI techniques consistently with established agent-related research while leveraging established DS techniques and concepts.

关键词： Computer architecture Problem-solving Knowledge engineering Organizations Collaboration Semantic Web Multi-agent systems Systems architecture Focusing distributed databases Architectural patterns distributed systems multi-agent systems entity-component-systems

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distributed Semi-Supervised Sparse Statistical Inference

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第6期70卷 4197-4217页

作者： Tu, Jiyuan Liu, Weidong Mao, Xiaojun Xu, Mingyue Shanghai Univ Finance & Econ Sch Math Shanghai 200433 Peoples R China Shanghai Jiao Tong Univ Sch Math Sci Minist Educ Key Lab Artificial Intelligence Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Math Sci Key Lab Sci & Engn Comp Minist Educ Shanghai 200240 Peoples R China Purdue Univ Dept Comp Sci W Lafayette IN 47906 USA

The debiased estimator is a crucial tool in statistical inference for high-dimensional model parameters. However, constructing such an estimator involves estimating the high-dimensional inverse Hessian matrix, incurring significant computational costs. This challenge becomes particularly acute in distributed setups, where traditional methods necessitate computing a debiased estimator on every machine. This becomes unwieldy, especially with a large number of machines. In this paper, we delve into semi-supervised sparse statistical inference in a distributed setup. An efficient multi-round distributed debiased estimator, which integrates both labeled and unlabelled data, is developed. We will show that the additional unlabeled data helps to improve the statistical rate of each round of iteration. Our approach offers tailored debiasing methods for M -estimation and generalized linear models according to the specific form of the loss function. Our method also applies to a non-smooth loss like absolute deviation loss. Furthermore, our algorithm is computationally efficient since it requires only one estimation of a high-dimensional inverse covariance matrix. We demonstrate the effectiveness of our method by presenting simulation studies and real data applications that highlight the benefits of incorporating unlabeled data.

关键词： distributed databases Computational modeling Manganese Computational efficiency Semisupervised learning Distance learning Data models distributed learning semi-supervised learning debiased lasso M-estimation generalized linear model

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Digital Thread for Smart Products: A Survey on Technologies, Challenges, and Opportunities in Service-Oriented Supply Chains

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IEEE ACCESS 2024年 12卷 125284-125305页

作者： Bianchini, Devis Fapanni, Tiziano Garda, Massimiliano Leotta, Francesco Mecella, Massimo Rula, Anisa Sardini, Emilio Univ Brescia Dept Informat Engn I-25123 Brescia Italy Univ Roma La Sapienza Dept Comp Control & Management Engn I-00185 Rome Italy

In Smart Manufacturing, the recent opportunities provided by the Information and Communication Technologies have paved the way to a seamless connection of the manufactured product throughout its entire lifecycle, leading to the diffusion of the concepts of Smart Product and Digital Thread, which leverage digital technologies to assure a continuous flow of data encompassing the design phase of a product, manufacturing, operation, maintenance and also its eventual disposal or recycling. This compelling need to obtain a unified view of information associated with Smart Products has stimulated the so-called Internet of Services (IoS) paradigm, allowing for the sharing of products data and the execution of functions among various participants in intertwined supply chains. In these contexts, service-oriented architectures are being more and more employed to meet the complex and ever-evolving data analysis requirements, particularly when implementing Digital Thread solutions for Smart Products, where several issues must be considered, ranging from the heterogeneity of (Big) data to data sovereignty and data access policies, as information may cross the borders of multiple actors participating in intertwined supply chains. This survey discusses about the technological solutions and challenges to implement Digital Threads for Smart Products in Smart Manufacturing contexts, providing insights on opportunities for future research directions. In addition, the survey proposes a comprehensive multi-tier service-oriented architectural model to jointly tackle (Big) data heterogeneity, data sovereignty and data access policies issues, as they are only partially addressed by the research efforts examined in the literature review.

关键词： Surveys Smart manufacturing Computer architecture Supply chains Instruction sets distributed databases Data models Service-oriented architecture Product delivery Cyber-physical systems Service-oriented architectures smart products digital thread Internet of Services cyber-physical production network smart factory

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