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   Articles with "distributed machine" as a keyword



Distributed machine learning strategies for efficient development of direct and inverse nonlinear and IIR models

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recommendations! Published in 2018 at "Journal of Ambient Intelligence and Humanized Computing"

DOI: 10.1007/s12652-018-0839-7

Abstract: This paper makes an in depth study on the applications of distributed machine learning based techniques for parameter estimation of infinite impulse response (IIR) systems and as well as inverse modeling of nonlinear systems or… read more here.

Keywords: machine learning; learning strategies; distributed machine; inverse ... See more keywords

2D-HRA: Two-Dimensional Hierarchical Ring-Based All-Reduce Algorithm in Large-Scale Distributed Machine Learning

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recommendations! Published in 2020 at "IEEE Access"

DOI: 10.1109/access.2020.3028367

Abstract: Gradient synchronization, a process of communication among machines in large-scale distributed machine learning (DML), plays a crucial role in improving DML performance. Since the scale of distributed clusters is continuously expanding, state-of-the-art DML synchronization algorithms… read more here.

Keywords: ring based; scale distributed; large scale; distributed machine ... See more keywords

Blockchain for Privacy Preserving and Trustworthy Distributed Machine Learning in Multicentric Medical Imaging (C-DistriM)

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recommendations! Published in 2020 at "IEEE Access"

DOI: 10.1109/access.2020.3029445

Abstract: The utility of Artificial Intelligence (AI) in healthcare strongly depends upon the quality of the data used to build models, and the confidence in the predictions they generate. Access to sufficient amounts of high-quality data… read more here.

Keywords: distrim; distributed learning; blockchain; distributed machine ... See more keywords

Dynamic Pricing and Placing for Distributed Machine Learning Jobs: An Online Learning Approach

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recommendations! Published in 2023 at "IEEE Journal on Selected Areas in Communications"

DOI: 10.1109/jsac.2023.3242707

Abstract: Nowadays distributed machine learning (ML) jobs usually adopt a parameter server (PS) framework to train models over large-scale datasets. Such ML job deploys hundreds of concurrent workers, and model parameter updates are exchanged frequently between… read more here.

Keywords: distributed machine; pricing; job; dynamic pricing ... See more keywords

When In-Network Computing Meets Distributed Machine Learning

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recommendations! Published in 2024 at "IEEE Network"

DOI: 10.1109/mnet.2024.3368138

Abstract: Emerging In-Network Computing (INC) technique provides a new opportunity to improve application’s performance by using network programmability, computational capability, and storage capacity enabled by programmable switches. One typical application is Distributed Machine Learning (DML), which… read more here.

Keywords: network computing; machine learning; distributed machine; dml systems ... See more keywords

Non-Clairvoyant Scheduling of Distributed Machine Learning With Inter-Job and Intra-Job Parallelism on Heterogeneous GPUs

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recommendations! Published in 2024 at "IEEE Transactions on Cloud Computing"

DOI: 10.1109/tcc.2024.3414440

Abstract: Distributed machine learning (DML) has shown great promise in accelerating model training on multiple GPUs. To increase GPU utilization, a common practice is to let multiple learning jobs share GPU clusters, where the most fundamental… read more here.

Keywords: parallelism; inter job; gpus; machine learning ... See more keywords

Joint Data Collection and Resource Allocation for Distributed Machine Learning at the Edge

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recommendations! Published in 2022 at "IEEE Transactions on Mobile Computing"

DOI: 10.1109/tmc.2020.3045436

Abstract: Under the paradigm of edge computing, the enormous data generated at the network edge can be processed locally. To make full utilization of these widely distributed data, we focus on an edge computing system that… read more here.

Keywords: system; distributed machine; machine learning; joint data ... See more keywords

A Scalable, High-Performance, and Fault-Tolerant Network Architecture for Distributed Machine Learning

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recommendations! Published in 2020 at "IEEE/ACM Transactions on Networking"

DOI: 10.1109/tnet.2020.2999377

Abstract: In large-scale distributed machine learning (DML), the network performance between machines significantly impacts the speed of iterative training. In this paper we propose BML, a scalable, high-performance and fault-tolerant DML network architecture on top of… read more here.

Keywords: scalable high; performance; distributed machine; machine learning ... See more keywords

Deep Learning-Based Job Placement in Distributed Machine Learning Clusters With Heterogeneous Workloads

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recommendations! Published in 2023 at "IEEE/ACM Transactions on Networking"

DOI: 10.1109/tnet.2022.3202529

Abstract: Nowadays, most leading IT companies host a variety of distributed machine learning (ML) workloads in ML clusters to support AI-driven services, such as speech recognition, machine translation, and image processing. While multiple jobs are executed… read more here.

Keywords: distributed machine; job; placement; interference ... See more keywords

DeepAutoD: Research on Distributed Machine Learning Oriented Scalable Mobile Communication Security Unpacking System

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recommendations! Published in 2022 at "IEEE Transactions on Network Science and Engineering"

DOI: 10.1109/tnse.2021.3100750

Abstract: The rapid growth of Android smart phones and abundant applications (Apps), a new security solution for distributed computing and mobile communications, has prompted many enhanced vendors to use different methods to effectively protect important Android… read more here.

Keywords: mobile communication; distributed machine; security; machine learning ... See more keywords

EdgeTB: a Hybrid Testbed for Distributed Machine Learning at the Edge with High Fidelity

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recommendations! Published in 2022 at "IEEE Transactions on Parallel and Distributed Systems"

DOI: 10.1109/tpds.2022.3144994

Abstract: Distributed Machine Learning (DML) at the edge has become an essential topic for providing low-latency intelligence near the data sources. However, both the development and testing of DML lack sufficient support. Reusable libraries that are… read more here.

Keywords: hybrid testbed; distributed machine; high fidelity; fidelity ... See more keywords