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Research on big data processing framework algorithm optimized by deep learning

Research on big data processing framework algorithm optimize...

引用

Electrical, Automation and Computer Engineering (ICEACE), International conference on

作者： Shuo Ma Business School Shandong University of Political Science and Law Jinan China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This paper proposes a deep learning Optimization big data processing framework based on Hadoop distributed architecture, Convolutional neural network (CNN) and Whale Optimization Algorithm (WOA). First, the Hadoop distributed file system (HDFS) and MapReduce technologies are used to store and preprocess large-scale data in parallel, and reduce data redundancy and processing delay through efficient data distribution and cleaning. Secondly, as a feature extraction module, the CNN model extracts the spatial features of the data layer by layer by using multi-layer convolution and pooling operations to provide reliable feature representation for subsequent classification and prediction tasks. In order to further improve the performance of the model, this paper introduces whale optimization algorithm (WOA) to optimize the hyperparameters of the CNN model (such as learning rate, convolution kernel size, etc.). By simulating the hunting behavior of whales, WOA automatically adjusts the combination of hyperparameters during model training, thus significantly improving the accuracy and training efficiency of the model. The experimental results show that the proposed Hadoop-CNN-WOA framework has higher accuracy than the traditional single model in processing large-scale image classification tasks.

关键词： Training Deep learning Convolution Computational modeling distributed databases Big Data Feature extraction Whale optimization algorithms Classification algorithms Convolutional neural networks

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WDMoE: Wireless distributed Large Language Models with Mixture of Experts

WDMoE: Wireless Distributed Large Language Models with Mixtu...

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IEEE conference on Global Communications (GLOBECOM)

作者： Nan Xue Yaping Sun Zhiyong Chen Meixia Tao Xiaodong Xu Liang Qian Shuguang Cui Ping Zhang Cooperative Medianet Innovation Center Shanghai Jiao Tong University China Department of Broadband Communication Peng Cheng Laboratory Shenzhen China Future Network of Intelligent Institute (FNii) the Chinese University of Hong Kong (Shenzhen) Shenzhen China Beijing University of Posts and Telecommunications Beijing China School of Science and Engineering (SSE) and the Future Network of Intelligent Institute (FNii) the Chinese University of Hong Kong (Shenzhen) Shenzhen China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, but how wireless networks can support LLMs has not been extensively studied. In this paper, we propose a wireless distributed LLMs paradigm based on Mixture of Experts (MoE), named WDMoE, through server-device collaboration at the wireless network edge. Specifically, we decompose the MoE layer in LLMs by deploying the gating network and the preceding neural network layer at the edge server of the base station (BS), while distributing the expert networks across the mobile devices. This arrangement leverages the parallel capabilities of expert networks on distributed devices. Moreover, to overcome the instability of wireless communications, we design an expert selection policy by taking into account both the performance of the model and the end-to-end latency, which includes both transmission delay and inference delay. Evaluations conducted across various LLMs and multiple datasets demonstrate that WDMoE not only outperforms existing models, such as Llama 2 with 70 billion parameters, but also significantly reduces end-to-end latency.

关键词： Performance evaluation Large language models Wireless networks neural networks Benchmark testing Mobile handsets Natural language processing Delays Servers Global communication

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A Stochastic Newton Algorithm for distributed Convex Optimization 35

A Stochastic Newton Algorithm for Distributed Convex Optimiz...

引用

35th Annual conference on neural Information processing Systems (NeurIPS)

作者： Bullins, Brian Patel, Kumar Kshitij Shamir, Ohad Srebro, Nathan Woodworth, Blake Toyota Technol Inst Chicago Chicago IL 60637 USA

ISBN: (纸本)9781713845393

We propose and analyze a stochastic Newton algorithm for homogeneous distributed stochastic convex optimization, where each machine can calculate stochastic gradients of the same population objective, as well as stochastic Hessian-vector products (products of an independent unbiased estimator of the Hessian of the population objective with arbitrary vectors), with many such stochastic computations performed between rounds of communication. We show that our method can reduce the number, and frequency, of required communication rounds compared to existing methods without hurting performance, by proving convergence guarantees for quasi-self-concordant objectives (e.g., logistic regression), alongside empirical evidence.

关键词： Stochastic systems

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distributed SPEECH SEPARATION IN SPATIALLY UNCONSTRAINED MICROPHONE ARRAYS

DISTRIBUTED SPEECH SEPARATION IN SPATIALLY UNCONSTRAINED MIC...

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IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Furnon, Nicolas Serizel, Romain Illina, Irina Essid, Slim Univ Lorraine CNRS INRIA Loria F-54000 Nancy France Inst Polytech Paris LTCI Telecom Paris Palaiseau France

ISBN: (纸本)9781728176055

Speech separation with several speakers is a challenging task because of the non-stationarity of the speech and the strong signal similarity between interferent sources. Current state-of-the-art solutions can separate well the different sources using sophisticated deep neural networks which are very tedious to train. When several microphones are available, spatial information can be exploited to design much simpler algorithms to discriminate speakers. We propose a distributed algorithm that can process spatial information in a spatially unconstrained microphone array. The algorithm relies on a convolutional recurrent neural network that can exploit the signal diversity from the distributed nodes. In a typical case of a meeting room, this algorithm can capture an estimate of each source in a first step and propagate it over the microphone array in order to increase the separation performance in a second step. We show that this approach performs even better when the number of sources and nodes increases. We also study the influence of a mismatch in the number of sources between the training and testing conditions.

关键词： Speech separation microphone arrays distributed processing

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RCIF: Towards Robust distributed DNN Collaborative Inference Under Highly Lossy networks

RCIF: Towards Robust Distributed DNN Collaborative Inference...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yujun Cheng Zhewei Zhang Shengjin Wang Department of Electronic Engineering Tsinghua University Beijing National Research Center for Information Science and Technology (BNRist) ZEEWAIN Technology Co. Ltd.

Collaborative Inference is a prospective paradigm for accelerating Deep neural network (DNN) inference by harnessing the computational resources of multiple devices. However, in highly lossy network environments, such as those encountered in wireless communication systems, the transmission loss of intermediate feature maps between devices can result in significant degradation of co-inference accuracy. In this paper, we first conduct a comprehensive investigation into the impact of intermediate feature map loss in real-world wireless scenarios and provide an in-depth analysis of loss patterns under UDP transmission. Motivated by these observations, we introduce Robust Co-inference Framework (RCIF), a novel framework that employs a hierarchical mask strategy to selectively drop activations at two different scales of feature maps. This approach enhances the robustness of DNN co-inference in the presence of network losses. Our evaluation on a variety of datasets and network architectures demonstrates that RCIF significantly enhances the accuracy and robustness of distributed DNN co-inference under highly lossy network conditions. Specifically, our results show that RCIF can achieve up to a 659% increase in accuracy compared to the original model under particularly poor network conditions.

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distributed Deep Convolutional network: A Generalized SVD Perspective

Distributed Deep Convolutional Network: A Generalized SVD Pe...

引用

International conference on Wireless Communications and Signal processing (WCSP)

作者： Yi Ding Junpeng Shi Shuyun Shi Jialong Qian Qinxian Chen College of Electronic Engineering National University of Defense Technology Hefei China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

With the rapid advancement of distributed systems technology, deep learning-based methods have become a common scheme to implement multiple data processing. This paper presents a novel multi-channel encoder-decoder architecture (called as D 2 CN), which is explained by integrating generalized singular value decomposition (GSVD) with the principles of Hankel convolution framelet. Specifically, we employ the feature extraction capability of GSVD to perform data interactions by forward/backward propagation, where numerous inputs are designed using the common bases and the reliable performance is achieved by training shared right bases. The network intuitively shows the interactions of multiple data in propagation, which is suitable for a wide range of inverse problems. Finally, we demonstrate the superiority of D 2 CN over other fundamental networks through numerical experiments conducted on inverse problem tasks.

关键词： Wireless communication Training Inverse problems Data processing Reliability engineering Robustness Real-time systems Convolutional neural networks Standards Singular value decomposition

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Machine Learning for Privacy Preservation with Minimal Information Leakage 3

Machine Learning for Privacy Preservation with Minimal Infor...

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3rd International conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Verma, Mr. Vikas Kaur, Simerjeet Vivekananda Global University Department of Mechanical Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

A wide range of application sectors are progressively using machine learning (ML). A successful machine learning (ML) model often requires a lot of training data and powerful processing resources, especially the newly developing deep neural network model. Due to the possibility of information leakage and the changing legislative landscapes that progressively limit Privacy-sensitive data access and use, the need for large amounts of available data, raises significant privacy concerns. Additionally, adversarial techniques. Therefore, carefully thought-out privacy-preserving machine learning (PPML) solutions are essential and are generating more and more research attention from academia and business. More and more PPML initiatives are being put forth through the incorporation of privacy-preserving methods into machine learning algorithms. As existing PPML techniques intersect with ML, systems, security, and privacy, among others, there is an important requirement to understand current research, associated barriers, and roadmaps for future research. This study offers a PGU model to help evaluate various PPML solutions by intricately breaking down each solution's privacy-preserving functions. It also evaluates and summarizes existing privacy-preserving methodologies in a methodical manner. Phase, Guarantee, and Technical Utility make up the PGU model's trinity. We also address the special features and difficulties of PPML and suggest future study avenues that will be beneficial to a wide variety of research communities in the fields of Machine learning, security systems, distributed systems and privacy system. © 2023 IEEE.

关键词： Deep neural networks

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Graph neural network Based on Spectral Parameter Estimation for Mobile networks

Graph Neural Network Based on Spectral Parameter Estimation ...

引用

International conference on Engineering and Emerging Technologies (ICEET)

作者： Renhong Jin Xin Yan School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

For online learning, Graph neural networks(GNNs) often require real graph data for parameter fine-tuning, but obtaining the graph data is challenging. Currently, GNNs are trained based on existing graph data. When learning in dynamic networks, spatial-domain GNNs use the adjacency matrix of entire graphs as its input, which are extremely resource-intensive in terms of computation and storage while dealing with large-scale graph data. Moreover, spectral-domain GNNs suffer from high spectral decomposition complexity. To enhance the generalization of GNN models and achieve lightweight GNN models, this paper proposes a lightweight GNN model based on spectral domain, SDGAT(Spectral-Domain Graph Attention network). By combining spectral domain graph convolution and attention mechanism, SDGAT can effectively capture the global structure and local features of a graph, which helps to suppress noise in the graph and improve the robustness of the model. Furthermore, a distributed estimation algorithm for dynamic network spectral parameters is proposed, which significantly reduces the computational overhead when processing large-scale graph data. Finally, authors validate the effectiveness and accuracy of SDGAT through classification and regression tasks, and explore the feasibility of extending this model to other GNNs.

关键词： Attention mechanisms Computational modeling Heuristic algorithms Transfer learning Estimation distributed databases Graph neural networks Data models distributed algorithms Spectral analysis

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Multi-focal-spot terahertz lenses designed using a neural network-based algorithm

Multi-focal-spot terahertz lenses designed using a neural ne...

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International conference on Infrared and Millimeter Waves

作者： Paweł Komorowski Mateusz Kałuża Przemysław Zagrajek Agnieszka Siemion Institute of Optoelectronics Military University of Technology Warsaw Poland Faculty of Physics Warsaw University of Technology Warsaw Poland

ISBN: (数字)9798350370324

ISBN: (纸本)9798350370331

The terahertz (THz) frequency band is expected to be used in future 6G wireless telecommunication systems. Irrespective of the exact technologies or bands, efficient beamforming of THz radiation is necessary. Therefore, the research on the multi-focal-spot THz lenses has been conducted and is shown in this paper. The novel neural network-based optimization method has been proposed to design elements forming circularly shaped and uniformly distributed focal spots. The improvement is observable for the structures redirecting the radiation into more than two asymmetrically distributed focal spots.

关键词： Wireless communication 6G mobile communication Optical design Array signal processing Optimization methods Terahertz radiation Optical fiber networks Telecommunications Terahertz communications Lenses

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PHYSICAL MODELING USING RECURRENT neural networkS WITH FAST CONVOLUTIONAL LAYERS 25

PHYSICAL MODELING USING RECURRENT NEURAL NETWORKS WITH FAST ...

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25th International conference on Digital Audio Effects, DAFx 2022

作者： Parker, Julian D. Schlecht, Sebastian J. Rabenstein, Rudolf Schäfer, Maximilian Native Instruments GmbH Berlin Germany Aalto Acoustics Lab Media Lab Aalto University Espoo Finland Erlangen Germany Erlangen Germany

ISBN: (纸本)9783200085992

Discrete-time modeling of acoustic, mechanical and electrical systems is a prominent topic in the musical signal processing literature. Such models are mostly derived by discretizing a mathematical model, given in terms of ordinary or partial differential equations, using established techniques. Recent work has applied the techniques of machine-learning to construct such models automatically from data for the case of systems which have lumped states described by scalar values, such as electrical circuits. In this work, we examine how similar techniques are able to construct models of systems which have spatially distributed rather than lumped states. We describe several novel recurrent neural network structures, and show how they can be thought of as an extension of modal techniques. As a proof of concept, we generate synthetic data for three physical systems and show that the proposed network structures can be trained with this data to reproduce the behavior of these systems. Copyright: © 2022 Julian D. Parker et al.

关键词： Recurrent neural networks

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