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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 5933-6017页

作者： Schoinas, Ioannis Triantafyllou, Anna Ioannidis, Dimosthenis Tzovaras, Dimitrios Drosou, Anastasios Votis, Konstantinos Lagkas, Thomas Argyriou, Vasileios Sarigiannidis, Panagiotis Univ Western Macedonia Dept Elect & Comp Engn Kozani 50150 Greece Ctr Res & Technol Hellas Informat Technol Inst Thessaloniki 57001 Greece Democritus Univ Thrace Dept Comp Sci Kavala 65404 Greece Kingston Univ Dept Networks & Digital Media London KT1 2EE England

Federated Learning has emerged as a revolutionary technology in Machine Learning (ML), enabling collaborative training of models in a distributed environment while ensuring privacy and security. This work discusses the topic of FL by providing insights into its various dimensions, perspectives, and components, leading to a comprehensive understanding of the technology. The survey begins by introducing the basic principles of FL and provides a high-level taxonomy of its methods. It continues by presenting application domains and associating challenges, categories and their applications. This mapping allows for an understanding of how particular challenges manifest in different contexts and applications. The main body delves into the various aspects of FL, including centralized and decentralized variants, methods for improving efficiency and effectiveness, and concerns regarding security, privacy, dynamic conditions, fairness, scalability and integration with other new technologies. Ultimately, the goal is to present recent advancements in these areas, along with new challenges and opportunities for future exploration. FL is poised to reshape the landscape of intelligent systems while promoting data privacy in decentralized and collaborative learning. Finally, this survey can serve as a reference point for methodological improvements as it highlights the strengths and weaknesses of existing approaches.

关键词： Security Internet of Things Surveys Medical services Artificial intelligence Optimization Privacy Federated learning machine learning artificial intelligence distributed computing distributed machine learning decentralized learning data heterogeneity personalized federated learning system heterogeneity communication efficiency security privacy fair federated learning scalability adaptive federated learning adaptive algorithms

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A Novel distributed Stack Ensembled Meta-Learning-Based Optimized Classification Framework for Real-time Prolific IoT Traffic Streams

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第8期47卷 9907-9930页

作者： Snehi, Manish Bhandari, Abhinav Punjabi Univ Comp Sci & Engn Patiala Punjab India

The concurrence of state-of-the-art Industrial 5G, Cyber-Physical Systems, Smart-Systems, Industrial Internet of Things, and Additive Manufacturing paves the next-level digital remodeling. However, the transfiguration unwittingly tailpiece an operational onus on the smart-environment operators. The multiplicity and classes of IoT devices operating in the intelligent environment are myriad. The characterization of ingress network traffic and the accurate classification of devices is necessary to efficiently manage the devices and offer cutting-edge security solutions and quality of Service (QoS). The paper addresses these challenges by offering a novel intelligent framework for traffic classification leveraging behavioral attributes of IoT traffic. The paper's contributions to the research community are fourfold. Firstly, the paper proposes a novel IoT classification framework based on Stack-Ensemble for real-time high-volume IoT traffic. The experimental results indicate that the proposed novel Stack Ensemble model can extract the best out of base models and demonstrate an accuracy of 99.94%. The intelligent models are evaluated over multiple dimensions to project the isometric view of the model performance and the experimental results. To achieve that goal, all the performance metrics that most researchers most often miss have been elucidated. Secondly, the paper comprehends the flow-level statistical characteristics of IoT devices. Third, the paper offers the distributed, scalable, and portable framework architecture. The architecture is horizontally scalable, distributing the computational load. The framework offers an end-to-end industry-grade machine-learning pipeline and triumphs the vulnerabilities of existing solutions. Finally, the paper discusses the statistical insights into the intelligent model and the results of the experimentation study. The proposed work paves the opportunities for researchers, smart-environment operators, and developers to unfold th

关键词： Classification Deep learning Docker distributed computing H2O Internet of Things IoT Machine learning Stack ensemble

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Benchmark for Personalized Federated Learning

IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY

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IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY 2024年第1期5卷 2-13页

作者： Matsuda, Koji Sasaki, Yuya Xiao, Chuan Onizuka, Makoto Osaka Univ Suita Osaka 5650871 Japan

Federated learning is a distributed machine learning approach that allows a single server to collaboratively build machine learning models with multiple clients without sharing datasets. Since data distributions may differ across clients, data heterogeneity is a challenging issue in federated learning. To address this issue, numerous federated learning methods have been proposed to build personalized models for clients, referred to as personalized federated learning. Nevertheless, no studies comprehensively investigate the performance of personalized federated learning methods in various experimental settings such as datasets and client settings. Therefore, in this article, we aim to benchmark the performance of existing personalized federated learning methods in various settings. We first survey the experimental settings in existing studies. We then benchmark the performance of existing methods through comprehensive experiments to reveal their characteristics in computer vision and natural language processing tasks which are the most popular tasks based on our survey. Our experimental study shows that (i) large data heterogeneity often leads to highly accurate predictions and (ii) standard federated learning methods (e.g. FedAvg) with fine-tuning often outperform personalized federated learning methods.

关键词： Benchmarking distributed computing Federated Learning

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Energy-efficient distributed password hash computation on heterogeneous embedded system

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AUTOMATIKA 2022年第3期63卷 399-417页

作者： Pervan, Branimir Knezovic, Josip Guberovic, Emanuel Univ Zagreb Fac Elect Engn & Comp Unska 3 Zagreb Croatia Green Light Technol Ltd Zagreb Croatia

This paper presents the improved version of our cool Cracker cluster (cCc), a heterogeneous distributed system for parallel and energy-efficient bcrypt password hash computation. The cluster consists of up to 8 computational units (nodes) with different performances measured in bcrypt hash computations per second [H/s]. In the cluster, nodes are low-power heterogeneous embedded systems with programmable logic containing specialized hash computation accelerators. In the experiments, we used a combination of Xilinx Zynq-series SoC boards and ZTEX 1.15y board which was initially used as a bitcoin miner. Zynq based nodes use the improved version of our custom bcrypt accelerator, which executes the most costly parts of the bcrypt hash computation in programmable logic. The cluster was formed around the famous open-source password cracking software package John the Ripper (abbr. JtR). On the communication layer, we used Message Passing Interface (MPI)library with a standard Ethernet network connecting the nodes. To mitigate the different performances among the cluster nodes and to balance the load, we developed and implemented password candidate distribution scheme based on the passwords' probability distribution, i.e. the order of appearance in the dictionary. We tested individual nodes and the cluster as a whole, trying different combinations of nodes and evaluating our distribution scheme for password candidates. We also compared our cluster with various GPU implementations in terms of performance, energy-efficiency, and price-efficiency. We show that our solution outperforms other platforms such as high-end GPUs, by a factor of at least 3 in terms of energy-efficiency and thus producing less overall cost of password attack than other platforms. In terms of the total operational costs, our cluster pays off after 4500 cracked passwords for a bcrypt hash with cost parameter 12, which makes it more appealing for real-world password-based system attacks. We also demonstrat

关键词： Bcrypt distributed computing energy efficiency heterogeneous hardware

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Iterative Sketching for Secure Coded Regression

IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY

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IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY 2024年 5卷 148-161页

作者： Charalambides, Neophytos Mahdavifar, Hessam Pilanci, Mert Hero, Alfred O., III Univ Calif San Diego Halicioglu Data Sci Inst La Jolla CA 92093 USA Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48104 USA Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA

Linear regression is a fundamental and primitive problem in supervised machine learning, with applications ranging from epidemiology to finance. In this work, we propose methods for speeding up distributed linear regression. We do so by leveraging randomized techniques, while also ensuring security and straggler resiliency in asynchronous distributed computing systems. Specifically, we randomly rotate the basis of the system of equations and then subsample blocks, to simultaneously secure the information and reduce the dimension of the regression problem. In our setup, the basis rotation corresponds to an encoded encryption in an approximate gradient coding scheme, and the subsampling corresponds to the responses of the non-straggling servers in the centralized coded computing framework. This results in a distributive iterative stochastic approach for matrix compression and steepest descent.

关键词： Coded computing gradient coding subspace embedding linear regression distributed computing compression

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SCIP: A scalable, reproducible, and open-source pipeline for morphological profiling image cytometry and microscopy data

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CYTOMETRY PART A 2024年第11期105卷 816-828页

作者： Lippeveld, Maxim Peralta, Daniel Filby, Andrew Saeys, Yvan VIB Ctr Inflammat Res Data Min & Modelling Biomed Ghent Belgium Univ Ghent Dept Appl Math Comp Sci & Stat Ghent Belgium Univ Ghent Dept Informat Technol IDLab Imec Ghent Belgium Newcastle Univ Biosci Inst & Innovat Methodol & Applicat Res Them Newcastle Upon Tyne England

Imaging flow cytometry (IFC) provides single-cell imaging data at a high acquisition rate. It is increasingly used in image-based profiling experiments consisting of hundreds of thousands of multi-channel images of cells. Currently available software solutions for processing microscopy data can provide good results in downstream analysis, but are limited in efficiency and scalability, and often ill-adapted to IFC data. In this work, we propose Scalable Cytometry Image Processing (SCIP), a Python software that efficiently processes images from IFC and standard microscopy datasets. We also propose a file format for efficiently storing IFC data. We showcase our contributions on two large-scale microscopy and one IFC datasets, all of which are publicly available. Our results show that SCIP can extract the same kind of information as other tools, in a much shorter time and in a more scalable manner.

关键词： data analysis distributed computing feature extraction imaging flow cytometry machine learning

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Memory-optimized distributed utility mining for big data

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第8期34卷 6491-6503页

作者： Kumar, Sunil Mohbey, Krishna Kumar Cent Univ Rajasthan Dept Comp Sci Ajmer India

In recent days, social media, online services, smartphones, and the Internet of Things (IoT) produces large quantities of data every second. The generated data is structured, unstructured, or semi-structured and available in various formats. Therefore, traditional approaches are not sufficient to handle such kind of data effectively. High utility pattern mining is a famous study area of data analytics that incorporates util-ity measures to consider user-based constraints such as number of units and benefit, in addition to fre-quency statistics of datasets. It is also essential to make effective decisions, and their demand is increasing in the last decade. Several techniques have been suggested for utility-based frequent pattern extraction. However, these approaches are limited to data size and operate on standalone systems. We have proposed a parallel approach named distributed memory-optimized utility mining (DMOUM) for high utility-based frequent pattern mining to address this issue. A new pruning technique is imple-mented that greatly reduces processing time and memory use. The proposed approach can handle huge amounts of data, i.e., big data. DMOUM approach is implemented in cluster-node architecture using Spark. The results are validated on various real-time datasets and found proposed approach has better performance than cutting edge approaches in terms of run-time, memory consumption, and scalability measures. The proposed work will provide solutions to real-time issues such as health care, education, e-commerce, and so on.(c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： High utility pattern identification distributed computing Parallel computing Spark Big data

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Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

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INFORMATION 2024年第1期15卷

作者： Wang, Xiaonan Guo, Yang Gao, Yuan Acad Mil Sci PLA Inst War Studies Beijing 100091 Peoples R China

Non-terrestrial network (NTN) is a trending topic in the field of communication, as it shows promise for scenarios in which terrestrial infrastructure is unavailable. Unmanned autonomous intelligent systems (UAISs), as a physical form of artificial intelligence (AI), have gained significant attention from academia and industry. These systems have various applications in autonomous driving, logistics, area surveillance, and medical services. With the rapid evolution of information and communication technology (ICT), 5G and beyond-5G communication have enabled numerous intelligent applications through the comprehensive utilization of advanced NTN communication technology and artificial intelligence. To meet the demands of complex tasks in remote or communication-challenged areas, there is an urgent need for reliable, ultra-low latency communication networks to enable unmanned autonomous intelligent systems for applications such as localization, navigation, perception, decision-making, and motion planning. However, in remote areas, reliable communication coverage is not available, which poses a significant challenge for intelligent systems applications. The rapid development of non-terrestrial networks (NTNs) communication has shed new light on intelligent applications that require ubiquitous network connections in space, air, ground, and sea. However, challenges arise when using NTN technology in unmanned autonomous intelligent systems. Our research examines the advancements and obstacles in academic research and industry applications of NTN technology concerning UAIS, which is supported by unmanned aerial vehicles (UAV) and other low-altitude platforms. Nevertheless, edge computing and cloud computing are crucial for unmanned autonomous intelligent systems, which also necessitate distributed computation architectures for computationally intensive tasks and massive data offloading. This paper presents a comprehensive analysis of the opportunities and challenges of unm

关键词： non-terrestrial network unmanned autonomous intelligent systems 5G/6G network unmanned aerial vehicle (UAV) mobile edge computing (MEC) cloud computing distributed computing artificial intelligent (AI)

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Large Scale Inference with Theoretical Guarantees Under Various distributed Settings

Large Scale Inference with Theoretical Guarantees Under Vari...

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作者： Shen, Shuting Harvard University

学位级别：Ph.D., Doctor of Philosophy

Large-scale inference under distributed settings is an essential problem in machine learning and statistical inference. In many applications, the amount of data is too large to be processed by a single machine, requiring distributed computing to achieve scalable and efficient computation. distributed inference involves splitting the data and computations across multiple machines, which brings challenges in terms of communication, computation, and synchronization. One important field that the distributed data settings might pose challenges to is principal component analysis (PCA), which is a widely used technique for dimensionality reduction and feature extraction. In view of the growing dimensionality and increasing sample sizes of modern big data, the traditional PCA algorithm is computationally expensive and does not scale well to large datasets. Besides, in federated ecosystems, the traditional PCA method is often not applicable due to privacy protection considerations and large communicational burden. Algorithms were proposed to lower the computational cost, but few can handle both high dimensionality and massive sample size under the distributed setting. In Chapter 1, we propose the FAst distributed (FADI) PCA method for federated data when both the dimension d and the sample size n are ultra-large, by simultaneously performing parallel computing along d and dis- tributed computing along n. Specifically, we utilize L parallel copies of p-dimensional fast sketches to divide the computing burden along d and aggregate the results distributetively along the split samples. We present FADI under a general framework applicable to multiple statistical problems, and establish comprehensive theoretical results under the general framework. We show that FADI enjoys the same non-asymptotic error rate as the traditional PCA when Lp ≥ d. We also derive inferential results that characterize the asymptotic distribution of FADI, and show a phase-transition phenomenon as Lp incre

关键词： distributed computing Doubly robust Large-scale inference Random matrix Representation learning

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Dynamic Replication Policy on HDFS Based on Machine Learning Clustering

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IEEE ACCESS 2023年 11卷 18551-18559页

作者： Ahmed, Motaz A. Khafagy, Mohamed H. Shaheen, Masoud E. Kaseb, Mostafa R. Fayoum Univ Fac Comp & Artificial Intelligence Dept Comp Sci Al Fayyum 63514 Egypt

Data growth in recent years has been swift, leading to the emergence of big data science. distributed File Systems (DFS) are commonly used to handle big data, like Google File System (GFS), Hadoop distributed File System (HDFS), and others. The DFS should provide the availability of data and reliability of the system in case of failure. The DFS replicates the files in different locations to provide availability and reliability. These replications consume storage space and other resources. The importance of these files differs depending on how frequently they are used in the system. So some of these files do not deserve to replicate many times because it is unimportant in the system. This paper introduces a Dynamic Replication Policy using Machine Learning Clustering (DRPMLC) on HDFS, which uses Machine Learning to cluster the files into different groups and apply other replication policies to each group to reduce the storage consumption, improve the read and write operations time and keep the availability and reliability of HDFS as a High-Performance distributed computing (HPDC).

关键词： Feature extraction File systems Machine learning Databases Big Data distributed computing Support vector machines Replicability Availability big data clustering Hadoop distributed file system high-performance distributed computing machine learning reliability replication policy

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