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Conference on Digital Avionics Systems (DASC)

作者： Jasmin Broehan Thimo Bielsky Nils Kuelper Frank Thielecke Institute of Aircraft Systems Engineering (FST) Hamburg University of Technology (TUHH) Hamburg Germany

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

The desire to further extend system functions on Integrated Modular Avionics (IMA) platforms necessitates advances in terms of module and network technology. The introduction of distributed computing concepts, such as passively cooled ARINC 653 Part 4 modules, is intended to enable fast local control loops for safety-critical systems on IMA, for which a central computing platform with an ARINC 664 backbone network would not meet timing requirements. Moreover, by mapping low-computing software applications close to their corresponding peripherals, a reduction of network load can be achieved. However, challenges persist during the conceptual design phase of IMA platforms, as peripheral localization and installation constraints induced by the aircraft geometry are difficult to consider due to high uncertainties. An approach that leverages parametric architecture design pattern within a model-based conceptual design framework is proposed. A use case study was conducted to identify conceptual variants of both state-of-the-Art distributed IMA and next-generation architectures that feature distributed computing for a Virtual Research Aircraft (ViRAC). The study demonstrated that a fully model-based, formalised approach that integrates different disciplinary tools during early design iterations can lead to a reasonable platform draft in fewer design iterations.

关键词： Uncertainty Computational modeling Atmospheric modeling Computer architecture Aerospace electronics Software Timing Aircraft distributed computing Next generation networking

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distributed non-disclosive validation of predictive models by a modified ROC-GLM

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BMC MEDICAL RESEARCH METHODOLOGY 2024年第1期24卷 1页

作者： Schalk, Daniel Rehms, Raphael Hoffmann, Verena S. Bischl, Bernd Mansmann, Ulrich Ludwig Maximilians Univ Munchen Dept Stat Munich Germany Ludwig Maximilians Univ Munchen Inst Med Informat Proc Biometry & Epidemiol Munich Germany Ludwig Maximilians Univ Munchen DIFUTURE DataIntegrat Future Med Munich Germany Ludwig Maximilians Univ Munchen Munich Ctr Machine Learning MCML Munich Germany

Background distributed statistical analyses provide a promising approach for privacy protection when analyzing data distributed over several databases. Instead of directly operating on data, the analyst receives anonymous summary statistics, which are combined into an aggregated result. Further, in discrimination model (prognosis, diagnosis, etc.) development, it is key to evaluate a trained model w.r.t. to its prognostic or predictive performance on new independent data. For binary classification, quantifying discrimination uses the receiver operating characteristics (ROC) and its area under the curve (AUC) as aggregation measure. We are interested to calculate both as well as basic indicators of calibration-in-the-large for a binary classification task using a distributed and privacy-preserving approach. Methods We employ DataSHIELD as the technology to carry out distributed analyses, and we use a newly developed algorithm to validate the prediction score by conducting distributed and privacy-preserving ROC analysis. Calibration curves are constructed from mean values over sites. The determination of ROC and its AUC is based on a generalized linear model (GLM) approximation of the true ROC curve, the ROC-GLM, as well as on ideas of differential privacy (DP). DP adds noise (quantified by the & ell;(2) sensitivity Delta(2)(f<^>) to the data and enables a global handling of placement numbers. The impact of DP parameters was studied by simulations. Results In our simulation scenario, the true and distributed AUC measures differ by Delta AUC< 0.01 depending heavily on the choice of the differential privacy parameters. It is recommended to check the accuracy of the distributed AUC estimator in specific simulation scenarios along with a reasonable choice of DP parameters. Here, the accuracy of the distributed AUC estimator may be impaired by too much artificial noise added from DP. Conclusions The applicability of our algorithms depends on the & ell;(2) sensitivity Delta

关键词： Area under the ROC curve distributed computing Medical tests ROC-GLM

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CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2024年第3期31卷 651-665页

作者： Mullie, Louis Afilalo, Jonathan Archambault, Patrick Bouchakri, Rima Brown, Kip Buckeridge, David L. Cavayas, Yiorgos Alexandros Turgeon, Alexis F. Martineau, Denis Lamontagne, Francois Lebrasseur, Martine Lemieux, Renald Li, Jeffrey Sauthier, Michael St-Onge, Pascal Tang, An Witteman, William Chasse, Michael Ctr Hosp Univ Montreal Dept Med Montreal PQ H2X 3E4 Canada Univ Montreal Fac Med Montreal PQ H3C 3J7 Canada Mila Quebec Artificial Intelligence Inst Montreal PQ H2S 3H1 Canada Jewish Gen Hosp Dept Med Montreal PQ H3T 1E4 Canada Univ Laval Dept Emergency Med & Family Med Quebec City PQ G1V 0A6 Canada Univ Laval Dept Anesthesiol & Crit Care Med Quebec City PQ G1V 0A6 Canada Ctr Rech Integre Syst Apprenant St Apprenant sant Ctr integre St & Serv Sociaux Chaudiere Appalaches Levis PQ G6V 3Z1 Canada Univ Montreal Ctr Hosp Univ Montreal Ctr Rech Montreal PQ H2X 0A9 Canada McGill Univ Sch Populat & Global Hlth Dept Epidemiol & Biostat Hlth Ctr Montreal PQ H3A 1G1 Canada Hop Sacre Coeur Montreal Dept Med Montreal PQ H4J 1C5 Canada Univ Laval CHU Quebec Ctr Rech Quebec City PQ G1V 4G2 Canada Ctr Hosp Univ Sherbrooke Ctr Rech CHUS Dept Anesthesiol & Crit Care Med Sherbrooke PQ J1G 2E8 Canada Univ Laval CHU Quebec Ctr Rech Laval PQ H3C 3J7 Canada Ctr Hosp Univ Montreal Dept Radiol Montreal PQ H2X 3E4 Canada Ctr Hosp Univ Montreal 1051 rue Sanguinet Montreal PQ H2X 0C1 Canada

Objectives: distributed computations facilitate multi-institutional data analysis while avoiding the costs and complexity of data pooling. Existing approaches lack crucial features, such as built-in medical standards and terminologies, no-code data visualizations, explicit disclosure control mechanisms, and support for basic statistical computations, in addition to gradient-based optimization *** and methods: We describe the development of the Collaborative Data Analysis (CODA) platform, and the design choices undertaken to address the key needs identified during our survey of stakeholders. We use a public dataset (MIMIC-IV) to demonstrate end-to-end multi-modal FL using CODA. We assessed the technical feasibility of deploying the CODA platform at 9 hospitals in Canada, describe implementation challenges, and evaluate its scalability on large patient ***: The CODA platform was designed, developed, and deployed between January 2020 and January 2023. Software code, documentation, and technical documents were released under an open-source license. Multi-modal federated averaging is illustrated using the MIMIC-IV and MIMIC-CXR datasets. To date, 8 out of the 9 participating sites have successfully deployed the platform, with a total enrolment of >1M patients. Mapping data from legacy systems to FHIR was the biggest barrier to *** and conclusion: The CODA platform was developed and successfully deployed in a public healthcare setting in Canada, with heterogeneous information technology systems and capabilities. Ongoing efforts will use the platform to develop and prospectively validate models for risk assessment, proactive monitoring, and resource usage. Further work will also make tools available to facilitate migration from legacy formats to FHIR and DICOM.

关键词： machine learning biomedical analytics healthcare data management distributed computing federated learning predictive models resource usage analysis

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Parallel and distributed Frugal Tracking of a Quantile

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FUTURE INTERNET 2024年第9期16卷 335页

作者： Epicoco, Italo Pulimeno, Marco Cafaro, Massimo Univ Salento Dept Engn Innovat I-73100 Lecce Italy

In this paper, we deal with the problem of monitoring network latency. Indeed, latency is a key network metric related to both network performance and quality of service, since it directly impacts on the overall user's experience. High latency leads to unacceptably slow response times of network services, and may increase network congestion and reduce the throughput, in turn disrupting communications and the user's experience. A common approach to monitoring network latency takes into account the frequently skewed distribution of latency values, and therefore specific quantiles are monitored, such as the 95th, 98th, and 99th percentiles. We present a comparative analysis of the speed of convergence of the sequential FRUGAL-1U, FRUGAL-2U, and EASYQUANTILE algorithms and the design and analysis of parallel, message-passing-based versions of these algorithms that can be used for monitoring network latency quickly and accurately. distributed versions are also discussed. Extensive experimental results are provided and discussed as well.

关键词： latency quantile stream parallel computing distributed computing

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distributed eQTL analysis with auxiliary information

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JOURNAL OF STATISTICAL PLANNING AND INFERENCE 2024年 228卷 34-45页

作者： Fang, Zhiwen Li, Gen Li, Wendong Pu, Xiaolong Xiang, Dongdong East China Normal Univ Sch Stat KLATASDS MOE Shanghai Peoples R China Univ Michigan Dept Biostat Ann Arbor MI USA Shanghai Univ Finance & Econ Shanghai Inst Int Finance & Econ Sch Stat & Management Shanghai Peoples R China

Expression quantitative trait locus (eQTL) analysis is a useful tool to identify genetic loci that are associated with gene expression levels. Large collaborative efforts such as the Genotype-Tissue Expression (GTEx) project provide valuable resources for eQTL analysis in different tissues. Most existing methods, however, either focus on one tissue at a time, or analyze multiple tissues to identify eQTLs jointly present in multiple tissues. There is a lack of powerful methods to identify eQTLs in a target tissue while effectively borrowing strength from auxiliary tissues. In this paper, we propose a novel statistical framework to improve the eQTL detection efficacy in the tissue of interest with auxiliary information from other tissues. This framework can enhance the power of the hypothesis test for eQTL effects by incorporating shared and specific effects from multiple tissues into the test statistics. We also devise data-driven and distributed computing approaches for efficient implementation of eQTL detection when the number of tissues is large. Numerical studies in simulation demonstrate the efficacy of the proposed method, and the real data analysis of the GTEx example provides novel insights into eQTL findings in different tissues. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： Auxiliary information distributed computing eQTL analysis Multiple testing

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Communication-Efficient distributed Gradient Descent via Random Projection

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STAT 2024年第4期13卷

作者： Qi, Haobo Gao, Yuan Beijing Normal Univ Sch Stat Beijing Peoples R China Peking Univ Guanghua Sch Management Beijing Peoples R China

The gradient descent algorithm is one type of communication-efficient algorithm in distributed computing as it only requires transmitting the gradient vectors to update parameters. However, as the dimensionality of model parameters increases, even transmitting the gradients alone can result in a significant amount of communication costs. On the other hand, many local computers in the distributed system may suffer from communication bandwidth limitations, so the transmission of the full gradients are practically prohibited. Therefore, reducing the communication cost of gradient descent algorithms, particularly the communication bandwidth, becomes an important issue. To address this problem, we propose a randomly projected gradient descent (RPGD) algorithm. The proposed algorithm consists of three main steps. First, the gradient computed by the local computers is compressed into a low-dimensional vector using a random matrix. Then, these projected gradients are transmitted to the central computer, aggregated and broadcasted back to the local ones. Finally, the local computers recover the gradients to their original dimensions and update the parameters. By employing random projection, we can reduce the communication bandwidth requirements in distributed computing while we can also provide better privacy protection for local computers. We have provided a theoretical convergence analysis of the RPGD algorithm. Extensive numerical studies have been conducted to demonstrate the finite sample performance of the proposed method.

关键词： communication efficient distributed computing gradient descent random projection

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Enhancing distributed agent environments with quantum multi-agent systems and protocols

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MULTIAGENT AND GRID SYSTEMS 2024年第2期20卷 109 - 127页

作者： Jenefa, A. Vidhya, K. Taurshia, Antony Naveen, V. Edward Kuriakose, Bessy M. Vijula, V. Karunya Inst Technol & Sci Sch Comp Sci & Technol Coimbatore Tamil Nadu India Sri Shakthi Inst Engn & Technol Dept Comp Sci & Engn Coimbatore Tamil Nadu India Malla Reddy Engn Coll Women Dept Comp Sci & Engn Hyderabad India

The utilization of Quantum Multi-Agent Systems (MAS) and Quantum Protocols in distributed agent environments has gained attention due to the need for enhanced protocol efficiency in quantum computing applications. Conventional methods often face limitations in achieving optimal performance, hindering the full potential of quantum computing in distributed settings. Existing approaches lack the necessary robustness to fully exploit the advantages offered by Quantum MAS, leading to inefficiencies in computational performance within distributed agent environments. In this context, we propose a novel Quantum MAS framework, which harnesses the principles of quantum superposition, entanglement, and advanced Quantum Protocols, including the quantum key distribution mechanism. The framework facilitates collaborative decision-making among agents through the utilization of joint quantum states and enables seamless synchronization of actions via the entanglement operator. The computational efficiency is optimized using quantum gate operations, thereby enhancing the overall computational performance in the distributed agent environment. We quantify the efficiency, showcasing the significant improvements achieved by the proposed Quantum MAS framework. Our research employs diverse datasets, including synthetic and real-world data, to comprehensively evaluate the performance and efficacy of the proposed Quantum MAS framework. Experimental results demonstrate a notable efficiency enhancement, with the proposed Quantum MAS achieving an average efficiency value of 0.92 across various experimental configurations and datasets. The findings underscore the significant potential of Quantum MAS in effectively addressing efficiency concerns within distributed agent environments, thus paving the way for broader applications of quantum computing in real-world scenarios.

关键词： Quantum multi-agent system quantum protocols distributed computing quantum cryptography quantum key distribution

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Slot selection algorithms in distributed computing

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JOURNAL OF SUPERcomputing 2014年第1期69卷 53-60页

作者： Toporkov, Victor Toporkova, Anna Tselishchev, Alexey Yemelyanov, Dmitry Natl Res Univ MPEI Moscow 111250 Russia Natl Res Univ Higher Sch Econ Moscow State Inst Elect & Math Moscow 109028 Russia European Org Nucl Res CERN CH-1211 Geneva 23 Switzerland

In this work, we introduce slot selection and co-allocation algorithms for parallel jobs in distributed computing with non-dedicated and heterogeneous resources. A single slot is a time span that can be assigned to a task, which is a part of a job. The job launch requires a co-allocation of a specified number of slots starting synchronously. The challenge is that slots associated with different resources of distributed computational environments may have arbitrary start and finish points that do not match. Some existing algorithms assign a job to the first set of slots matching the resource request without any optimization (the first fit type), while other algorithms are based on an exhaustive search. In this paper, algorithms for effective slot selection of linear complexity on an available slots number are studied and compared with known approaches. The novelty of the proposed approach consists of allocating alternative sets of slots. It provides possibilities to optimize job scheduling.

关键词： distributed computing Economic scheduling Resource management Slot Job Task Batch

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A distributed algorithm for directed minimum-weight spanning tree

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distributed computing 2023年第1期36卷 57-87页

作者： Fischer, Orr Oshman, Rotem Tel Aviv Univ Comp Sci Dept Tel Aviv Israel

In the directed minimum spanning tree problem (DMST, also called minimum weight arborescence), we are given a directed weighted graph, and a root node r. Our goal is to construct a minimum-weight directed spanning tree, rooted at r and oriented outwards. We present the first sub-quadraticDMSTalgorithm in the distributed CONGEST network model, where the messages exchanged between the network nodes are bounded in size. We consider three versions of the model: a network where the communication links are bidirectional but can have different weights in the two directions;a network where communication is unidirectional;and the Congested Clique model, where all nodes can communicate directly with each other. Our DMST algorithm is based on a variant of Lovasz' DMST algorithm for the PRAM model, and uses a distributed single-source shortest-path (SSSP) algorithm for directed graphs as a black box. In the bidirectional CONGEST model, our algorithm has roughly the same running time as the SSSP algorithm that is used as a black box;using the state-of-the-art SSSP algorithm due to Chechik and Mukhtar (in: Symposium on principles of distributed computing (PODC), ACM, 2020, pp 464-473), we obtain a running time of (O) over tilde(root nD(1/4) + D)) rounds for the bidirectional communication case. For the unidirectional communication model we give an (O) over tilde (n) algorithm, and show that it is nearly optimal. And finally, for the Congested Clique, our algorithm again matches the best known SSSP algorithm: it runs in (O) over tilde (n(1/3)) rounds. On the negative side, we adapt an observation of Chechik in the sequential setting to show that in all three models, the DMST problem is at least as hard as the (s, t)-shortest path problem. Thus, in terms of round complexity, distributed DMST lies between single-source shortest path and (s, t)-shortest path.

关键词： distributed computing Directed minimum spanning tree Arborescence CONGEST

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distributed hybrid quantum-classical performance prediction for hyperparameter optimization

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QUANTUM MACHINE INTELLIGENCE 2024年第2期6卷 1-14页

作者： Wulff, Eric Amboage, Juan Pablo Garcia Aach, Marcel Gislason, Thorsteinn Eli Ingolfsson, Thorsteinn Kristinn Ingolfsson, Tomas Kristinn Pasetto, Edoardo Delilbasic, Amer Riedel, Morris Sarma, Rakesh Girone, Maria Lintermann, Andreas CERN Espl Particules 1 CH-1211 Geneva Switzerland Forschungszentrum Julich Julich Supercomp Ctr Wilhelm Johnen Str D-52428 Julich Germany Univ Iceland Sch Engn & Nat Sci IS-107 Reykjavik Iceland Rhein Westfal TH Aachen D-52056 Aachen Germany

Hyperparameter optimization (HPO) of neural networks is a computationally expensive procedure, which requires a large number of different model configurations to be trained. To reduce such costs, this work presents a distributed, hybrid workflow, that runs the training of the neural networks on multiple graphics processing units (GPUs) on a classical supercomputer, while predicting the configurations' performance with quantum-trained support vector regression (QT-SVR) on a quantum annealer (QA). The workflow is shown to run on up to 50 GPUs and a QA at the same time, completely automating the communication between the classical and the quantum systems. The approach is evaluated extensively on several benchmarking datasets from the computer vision (CV), high-energy physics (HEP), and natural language processing (NLP) domains. Empirical results show that resource costs for performing HPO can be reduced by up to 9% when using the hybrid workflow with performance prediction, compared to using a plain HPO algorithm without performance prediction. Additionally, the workflow obtains similar and in some cases even better accuracy of the final hyperparameter configuration, when combining multiple heuristically obtained predictions from the QA, compared to using just a single classically obtained prediction. The results highlight the potential of hybrid quantum-classical machine learning algorithms. The workflow code is made available open-source to foster adoption in the community.

关键词： Hyperparameter optimization Quantum annealing Hyperband distributed computing

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