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检索条件"任意字段=9th IEEE International Conference on Distributed Computing in Sensor Systems, DCoSS 2013"

共 236 条记录，以下是151-160 订阅

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Communication Analysis for Multidimensional Parallel Training of Large-scale DNN Models 25

Communication Analysis for Multidimensional Parallel Trainin...

引用

25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Lai, Zhiquan Hao, Yanqi Li, Shengwei Li, Dongsheng College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798350330014

Multidimensional parallel training has been widely applied to train large-scale deep learning models like GPT-3. the efficiency of parameter communication among training devices/processes is often the performance bottleneck of large model training. Analysis of parameter communication mode and traffic has important reference significance for the research of interconnection network design and computing task scheduling to improve the training performance. In this paper, we analyze the parametric communication modes in typical 3D parallel training (data parallelism, pipeline parallelism, and tensor parallelism), and model the traffic in different communication modes. Finally, taking GPT-3 as an example, we present the communication in its 3D parallel training. © 2023 ieee.

关键词： Deep neural networks

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Version Control for Authentication in distributed Reprogramming in Wireless sensor Networks 9th

Version Control for Authentication in Distributed Reprogramm...

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9th international conference on Advanced computing and Communication Technologies (ICACCT)

作者： Shinde, Tejal M. Chhatani, Reeta Sardar Patel Inst Technol Bombay Maharashtra India

ISBN: (纸本)9789811010231;9789811010217

Reprogramming is a technique used in wireless sensor networks WSN) to update the software part of sensor nodes from remote locations over wireless links during run time of the network. However, reprogramming needs to be integrated with secure and efficient authentication technique to avoid any unauthorized access to sensor nodes. this paper gives an efficient authentication technique for distributed reprogramming using version numbers from version control operation of reprogramming. the Existing version control technique is modified to achieve both authentication and version control in reprogramming. the proposed method is compared with the two other algorithms defined for authentication of users in Das, Two-factor user authentication in wireless sensor networks. ieee Trans Wireless Commun 8: 1086-1090, 2009) [3] and He et al, SDRP: a secure and distributed reprogramming protocol for wireless sensor networks. ieee Trans Ind Electron 59: 4155-4163, 2012) [5]. the comparative analysis shows version control method is more efficient and is robust to impersonation attack.

关键词： Reprogramming Wireless sensor networks Authentication distributed reprogramming Version control

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the Rising Prospects of Cloud Robotic Applications

The Rising Prospects of Cloud Robotic Applications

引用

ieee 9th international conference on Computational Cybernetics (ICCC)

作者： Jordan, S. Haidegger, T. Kovacs, L. Felde, I. Rudas, I. Budapest Univ Technol & Econ BME Budapest Hungary Obuda Univ Budapest Hungary

ISBN: (纸本)9781479900633;9781479900602

Cloud Robotics is an emerging field within robotics, currently covering various application domains and robot network paradigms. this paper provides a structured, systematic overview of the numerous definitions, concepts and technologies linked to Cloud Robotics and cloud technologies in a broader sense. It also presents a roadmap for the near future, describing development trends and emerging application areas. Cloud Robotics may have a significant role in the future as an explicitly human-centered technology, capable of addressing the dire needs of our society.

关键词： cloud robotics distributed systems cloud computing networked robotics

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CloudDRN: A Lightweight, End-to-End System for Sharing distributed Research Data in the Cloud

CloudDRN: A Lightweight, End-to-End System for Sharing Distr...

引用

ieee 9th international conference on e-Science (e-Science)

作者： Humphrey, Marty Steele, Jacob Kim, In Kee Kahn, Michael G. Bondy, Jessica Ames, Michael Univ Virginia Dept Comp Sci Charlottesville VA 22903 USA Univ Colorado Anschutz Med Camp Boulder CO 80309 USA

ISBN: (纸本)9780769550831

the cloud has proven itself as a scalable platform for Web-based applications. However, scientists and medical researchers are still searching for a simple cloud-based architecture that enables secure collaboration and sharing of distributed datasets. To date, attempts at using the cloud for this purpose generally view the cloud as simply a pool of servers upon which to run their legacy software. this approach fails to leverage the unique platform capabilities of the cloud. In this paper, we describe our Cloud distributed Research Network (CloudDRN). We leverage the cloud for availability, reliability, scalability, and improved security as compared to legacy distributed systems while still supporting site autonomy. Our philosophy is to adapt commercial software tooling that was originally designed for business use-cases, thereby benefiting from the large built-in user community. We describe our general architecture and show an example of our system created to share distributed clinical research data. We evaluate our system in Amazon Web Services (AWS) and in Microsoft Windows Azure and find that while each cloud achieves similar financial cost, representative queries are 3.5x slower on average in Windows Azure.

关键词： distributed systems cloud computing data

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Spectral Clustering Based Resource Allocation Strategy for distributed Large-scale Network Emulation 25

Spectral Clustering Based Resource Allocation Strategy for D...

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作者： Chen, Jiyan Gu, Rentao Bai, Lin Sch. of Information and Communication Engineering Beijing University of Posts and Telecommunications State Key Laboratory of Information Photonics and Optical Communications Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350330014

Nowadays, networks are increasingly reliant on software frameworks and virtualization. To obtain relevant pre-dictions of the behavior of their protocols, network emulation tools play a crucial role. However, as networks grow in size and complexity, the emulation task demands more physical resources such as CPU and memory. Consequently, a single physical machine is no longer sufficient to handle large-scale network emulation. Instead, distributed network emulation tools must be employed for resource-intensive network emulation tasks. In resource-intensive distributed network emulation, the physical resource allocation is NP-hard to perform appropriately. In this paper, we propose SCBG (spectral clustering based greedy node placement algorithm) as a means to enhance the bandwidth utilization between physical nodes in distributed emulation. SCBG aims to address the communication bottleneck in distributed network simulation, surpassing the algorithm proposed by Distrinet, while also improving the algorithm's execution speed. Additionally, SCBG incorporates optimization for time dilation when physical bandwidth resources are severely insufficient. this optimization leads to a reduction in the additional time required for network simulation after applying time dilation. Experimental results demonstrate that SCBG surpasses the algorithm in Distrinet across a diverse range of intricate large-scale networks. © 2023 ieee.

关键词： Optimization

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Solar Irradiance Forecasting Utilizing distributed and Federated Learning 25

Solar Irradiance Forecasting Utilizing Distributed and Feder...

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作者： Zhang, Hao Yan, Ke College of Information Engineering China Jiliang University Hangzhou China China Jiliang University National University of Singapore Singapore

ISBN: (纸本)9798350330014

Photovoltaic energy generation and prediction are crucial for the integration between solar plants and smart grid. Accurate solar radiation prediction is one of the most important research topics for solar energy generation. Existing deep learning methods require sufficient data for training for achieving the desired results. In traditional deep learning modeling, the data sets required for model training usually come from various data in different regions and organizations, and it is difficult to ensure data security and privacy protection. Federated learning can effectively utilize the data optimization model while ensuring data security and privacy, but there is still the risk of privacy leakage by analyzing the parameters uploaded by the client. To effectively utilize data optimization models while ensuring data security and privacy, we propose a method based on a federated learning framework and N-BEATS to achieve interpretable time series prediction of solar irradiance. the model builds the N-BEATS model on the solar irradiance datasets of multiple institutions in a distributed method, does not share local data to protect institutional data privacy, the concept of differential privacy (DP) is introduced, Gaussian noise is added to the parameters of the client before the server-side aggregation processing, the model parameters of each distributed node are collected through TCP communication for federated average, and the parameters are returned to each distributed node. the experimental results show that compared with other deep learning models, the model has better prediction accuracy under the premise of ensuring data security and privacy. © 2023 ieee.

关键词： Solar radiation

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Ensuring compliance of distributed and collaborative workflows

Ensuring compliance of distributed and collaborative workflo...

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9th ieee international conference on Collaborative computing: Networking, Applications and Worksharing, COLLABORATECOM 2013

作者： Knuplesch, David Reichert, Manfred Pryss, Rüdiger Fdhila, Walid Rinderle-Ma, Stefanie Institute of Databases and Information Systems Ulm University Germany Faculty of Computer Science University of Vienna Austria

ISBN: (纸本)9781936968923

Automated workflows must comply with domain-specific regulations, standards and rules. So far, compliance issues have been mainly addressed in the context of intra-organizational workflows. In turn, there exists only little work dealing with compliance of distributed and collaborative workflows. As opposed to intra-organizational workflows, for distributed and collaborative workflows compliance must be addressed at different levels. this includes local compliance rules of a particular partner as well as global compliance rules to be obeyed by multiple partners collaborating in the distributed workflow. As a particular challenge, the private elements of a particular partner workflow are hidden to the partners and hence not known by them. Accordingly, only limited information is available when checking compliance of distributed and collaborative workflows. this paper introduces techniques enabling compliance checking for distributed and collaborative workflows, taking these privacy constraints into account. Hence it enables ensuring compliance of distributed and collaborative workflows at design time. © 2013 ICST.

关键词： Compliance control

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Efficient Large Models Fine-tuning on Commodity Servers via Memory-balanced Pipeline Parallelism 25

Efficient Large Models Fine-tuning on Commodity Servers via ...

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作者： Liu, Yujie Lai, Zhiquan Liu, Weijie Wang, Wei Li, Dongsheng College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798350330014

Large models have achieved impressive performance in many downstream tasks. Using pipeline parallelism to fine-tune large models on commodity GPU servers is an important way to make the excellent performance of large models available to the general public. Previous solutions fail to achieve an efficient memory-balanced pipeline parallelism. In this poster, we introduce a memory load-balanced pipeline parallel solution. this solution balances memory consumption across stages on commodity GPU servers via NVLink bridges. It establishes a new pathway to offload data from GPU to CPU by using the PCIe link of adjacent GPUs connected by the NVLink bridge. Furthermore, our method orchestrates offload operations to minimize the offload latency during large model fine-tuning. Experiments demonstrate that our solution can balance the memory footprint among pipeline stages without sacrificing training performance. © 2023 ieee.

关键词： Program processors

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VPFL: A Verifiable Property Federated Learning Framework Against Invisible Attacks in distributed IoT 25

VPFL: A Verifiable Property Federated Learning Framework Aga...

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作者： Wu, Yuanxiang Cheng, Hang Guan, Lin Liu, Peihao Chen, Fei Wang, Meiqing College of Computer Science and Big Data Fuzhou University Fuzhou China

ISBN: (纸本)9798350330014

the development of Federated Learning (FL) offers an efficient Machine Learning (ML) approach with privacy protection to solve the data island issue in distributed Internet of things (IoT). However, existing FL frameworks still suffer from invisible attacks in IoT environments, such as free-rider attacks, backdoor attacks, and model theft. In this paper, we propose a Verifiable Property Federated Learning (VPFL) framework to overcome the above invisible attacks. We present a black-box watermarking task distribution mechanism to prevent free-rider attacks by verifying the property of local models. Our adversarial fine-tuning embedding technique can not only eliminate backdoors in global models, but also simultaneously embed white-box watermarks into model parameters to prevent model theft. Comprehensive experimental evaluations demonstrate that our framework outperforms state-of-the-art schemes in terms of security and feasibility against invisible attacks. © 2023 ieee.

关键词： Internet of things

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distributed computing framework of intelligent sensor network for electric power internet of things 9

Distributed computing framework of intelligent sensor networ...

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9th ieee Joint international Information Technology and Artificial Intelligence conference, ITAIC 2020

作者： Li, Dawei Yu, Ruimo Song, Chunhe Li, Shu Jia, Gengtao Zhou, Xiaoli Start Grid Shanghai Information Telecommunication Company Shanghai200122 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

the power IoT terminal environment is complex, and current smart sensor sensors have problems such as poor networking capabilities and large processing delays, and few specific edge-side automatic networking capabilities. this paper analyzes and elaborates the distributed intelligent sensor computing framework from the perspective of edge intelligent networking, designs a computing model for intelligent sensors of power Internet of things terminals, and gives the edge-based deployment of convolutional neural network architecture, data aggregation, model training, and intelligent reasoning. Finally, the validity of the proposed framework model is verified by public sample data. © 2020 ieee.

关键词： Internet of things

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