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检索条件"任意字段=Conference on Neural Network and Distributed Processing"

共 2988 条记录，以下是1-10 订阅

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PP-Stream: Toward High-Performance Privacy-Preserving neural network Inference via distributed Stream processing 40

PP-Stream: Toward High-Performance Privacy-Preserving Neural...

引用

40th IEEE International conference on Data Engineering, ICDE 2024

作者： Liu, Qingxiu Huang, Qun Chen, Xiang Wang, Sa Wang, Wenhao Han, Shujie Lee, Patrick P. C. Peking University China The Chinese University of Hong Kong Hong Kong Zhejiang University China Institute of Computing Technology Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China

ISBN: (纸本)9798350317152

Privacy preservation is critical for neural network inference, which often involves collaborative execution of different parties to make predictions on sensitive data based on sensitive neural network models. However, the expensive cryptographic operations of privacy preservation also pose performance chal-lenges to neural network inference. We address this performance-security tension by designing PP-Stream, a distributed stream processing system for high-performance privacy-preserving neural network inference. PP-Stream adopts hybrid privacy-preserving mechanisms for linear and non-linear operations of neural network inference. It treats inference data as real-time data streams, and parallelizes the inference operations across multiple pipelined stages that are executed by multiple servers and threads. It also solves the load-balanced resource allocation across servers and threads as an optimization problem. We prototype PP-Stream and show via testbed experiments that it achieves low inference latencies on various neural network models. © 2024 IEEE.

关键词： Sensitive data

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Agent-Based Medical Image processing Using Multi-stage distributed neural network 17th

Agent-Based Medical Image Processing Using Multi-stage Distr...

引用

17th International KES conference on Agents and Multi-Agent Systems: Technologies and Applications, KES-AMSTA 2023

作者： Stranjak, Armin Campagna, Swen Čavrak, Igor Siemens Healthcare GmbH Allee am Röthelheimpark 2 Erlangen91052 Germany Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb10000 Croatia

ISBN: (纸本)9789819930678

In this paper, we describe possible applications of early exit deep neural networks in magnetic resonance imaging, aiming to improve patient scan times and reduce processing costs. The solutions rely on deep neural network layers deployed over a hierarchical processing environment of embedded, edge-based and shared cloud-based resources. Coordination among processing environment layers is achieved by representing different processing resources as agents, effectively forming a multi-agent system. The described approach combines the least-required processing property of the early exit deep neural network and the resource usage optimisation of the multi-agent system in the dynamic and open environment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Proceedings - 30th Euromicro International conference on Parallel, distributed and network-Based processing, PDP 2022

Proceedings - 30th Euromicro International Conference on Par...

引用

30th Euromicro International conference on Parallel, distributed and network-Based processing, PDP 2022

ISBN: (纸本)9781665469586

The proceedings contain 42 papers. The topics discussed include: an efficient compilation of coarse-grained reconfigurable architectures utilizing pre-optimized sub-graph mappings;evaluating micro-batch and data frequency for stream processing applications on multi-cores;a parallel approximation algorithm for the steiner forest problem;exploiting vector extensions to accelerate time series analysis;a neural network to estimate isolated performance from multi-program execution;a heuristic for constructing minimum average stretch spanning tree using betweenness centrality;accelerating distributed deep reinforcement learning by in-network experience sampling;parallel integer multiplication;advancing database system operators with near-data processing;and clustering datasets in cloud computing environment for user identification.

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PP-Stream: Toward High-Performance Privacy-Preserving neural network Inference via distributed Stream processing

PP-Stream: Toward High-Performance Privacy-Preserving Neural...

引用

International conference on Data Engineering

作者： Qingxiu Liu Qun Huang Xiang Chen Sa Wang Wenhao Wang Shujie Han Patrick P. C. Lee Peking University The Chinese University of Hong Kong Zhejiang University Institute of Computing Technology Chinese Academy of Sciences Institute of Information Engineering Chinese Academy of Sciences

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

关键词： Privacy Tensors Instruction sets neural networks Prototypes Data models Real-time systems

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Research on a new method of islanding detection based on lifting wavelet and neural network

Research on a new method of islanding detection based on lif...

引用

International conference on Electronics, Electrical and Control System (EECS)

作者： Xie, Dong Zang, Dajin Gao, Peng Tongling Univ Dept Elect Engn Tongling Peoples R China

At present, the commonly used active and passive islanding detection methods have their own shortcomings, and the islanding detection effect is difficult to meet the requirements. Therefore, a new detection method based on wavelet signal processing and artificial intelligence to identify islanding is proposed in this paper. In this method, the feature quantities required for islanding detection are obtained by wavelet transform and signal processing, and then the feature quantities are identified by neural network to determine whether the islanding is generated in distributed generation *** transformation has a strong ability of signal feature extraction, while the neural network has strong learning and identification abilities, the combination of the both is beneficial to improve the success rate of islanding detection. Simulation verification shows that the new islanding detection method proposed in this paper can detect islanding quickly and accurately, and the performance of islanding detection has been significantly improved.

关键词： distributed generation islanding detection wavelet analysis neural network

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STATE-AUGMENTED INFORMATION ROUTING IN COMMUNICATION SYSTEMS WITH GRAPH neural networkS 49

STATE-AUGMENTED INFORMATION ROUTING IN COMMUNICATION SYSTEMS...

引用

49th IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Das, Sourajit NaderiAlizadeh, Navid Ribeiro, Alejandro Univ Penn Philadelphia PA 19104 USA Duke Univ Durham NC USA

ISBN: (纸本)9798350344868;9798350344851

We consider the problem of routing network packets in a large-scale communication system where the nodes have access to only local information. We formulate this problem as a constrained learning problem, which can be solved using a distributed optimization algorithm. We approach this distributed optimization using a novel state-augmentation (SA) strategy to maximize the aggregate information packets at different source nodes, leveraging dual variables corresponding to flow constraint violations. The construction is based on graph neural networks (GNNs) that employ graph convolutions over the underlying communication network topology. We devise an unsupervised learning algorithm to transform the output of the GNN architecture into optimal routing decisions. The proposed method takes advantage of only the local information available at each node and efficiently routes the desired packets to the destination. We provide numerical results demonstrating the superiority of the proposed method over baseline routing algorithms.

关键词： Routing distributed optimization graph neural networks unsupervised learning Lagrangian duality

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Precision DDoS Detection through Gaussian Noise-Augmented neural networks 15

Precision DDoS Detection through Gaussian Noise-Augmented Ne...

引用

15th International conference on networks of the Future

作者： Alfatemi, Ali Oliveira, Dingo Rahouti, Mohamed Hafid, Abdelatif Ghani, Nasir Fordham Univ Comp & Informat Sci Dept Bronx NY 10458 USA Penn State Univ Coll IST Monaca PA 15601 USA ESISA ESISA Analyt Lab Fes 30000 Morocco Univ S Florida Dept Elect Engn Tampa FL 33620 USA

ISBN: (纸本)9798350377774;9798350377767

The detection of distributed Denial of Service (DDoS) attacks is a critical challenge in network security, requiring effective and efficient solutions to safeguard data and services. This paper addresses this problem by introducing a neural network model specifically designed for DDoS attack detection. The model employs a streamlined architecture to ensure rapid processing times and high performance. A key innovation is the integration of Gaussian noise, which enhances the robustness and generalization capabilities of the model. Extensive experiments validate the effectiveness and resilience of this approach, demonstrating its practicality for real-world network security applications. The findings highlight the significant role of noise regularization in improving the reliability of neural network models for detecting cyber threats.

关键词： Deep learning distributed Denial of Service Gaussian Noise neural network

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Fed-IoVIDS: Intrusion Detection based on Attack Behavior Analysis with Temporal Model on IoV Considering Privacy Protection

Fed-IoVIDS: Intrusion Detection based on Attack Behavior Ana...

引用

International Joint conference on neural networks (IJCNN)

作者： Chen, Rui Zhao, Jing Dalian Univ Technol Sch Software Technol Dalian 116024 Liaoning Peoples R China

ISBN: (纸本)9798350359329;9798350359312

With advancements in distributed communications for the IoV, security threats expose significant challenges. While current IoV intrusion detection systems demonstrate high accuracy, they rely heavily on private or easily forged data. Moreover, the training process incurs increased network communication costs, fails to protect user privacy, and distorted data degrades detection performance. To address these limitations, we proposes a distributed federated learning-based intrusion detection model for IoV using non-private behavior features. Firstly, we design a data processing algorithm that groups and slices IoV communication messages into time series. Then, behavior vectors are extracted using an attention-based time series model designed in this work. Attacks are detected by spatially transforming the residuals with a neural network. Finally, we use a federated learning algorithm for data processing and training of the model, effectively reduce communication burden and protect privacy training data on the vehicle-side. Extensive experiments on two datasets validate the proposed model, achieving F1 scores of 91.66% and 90.25% respectively, outperforming state-of-theart methods. We publicly release the model and algorithms to improve reproducibility and accessibility of effective IoV intrusion detection solutions.

关键词： distributed Communication Attack Behavior analysis Intrusion Detection Privacy Protection federated learning

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A Fast Machine Learning Framework with distributed Packet Loss Tolerance 24

A Fast Machine Learning Framework with Distributed Packet Lo...

引用

2nd Asia conference on Computer Vision, Image processing and Pattern Recognition (CVIPPR)

作者： Wu, Shuang East China Normal Univ Sch Data Sci & Engn Putuo Shanghai Peoples R China

ISBN: (纸本)9798400716607

Growing dataset and model sizes for Deep neural networks (DNNs) training have necessitated distributed training. Despite a rich literature on designing better distributed training algorithms and frameworks, few of them touch the lower layers (e.g. transport layer) of TCP/IP model. There's a recent paper [11] calling for rethinking the transport layer for distributed training, which suggests by simulation the potential performance gain from redesigning a new transport layer. Building on this premise, our research identifies TCP's limitations for distributed ML in data centers and introduces a Parameter Server (PS) system with a bounded-loss communication layer. It validates gradient-loss-tolerant neural networks for ML tasks and enhances distributed training efficiency through a novel communication protocol. Preliminary results show that such a method could potentially improve the performance by 8.2X, reinforcing the feasibility of scalable and efficient distributed ML systems.

关键词： machine learning neural networks network distributed machine learning

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Complex-Valued Physics-Informed neural network for Near-Field Acoustic Holography 32

Complex-Valued Physics-Informed Neural Network for Near-Fiel...

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32nd European Signal processing conference (EUSIPCO)

作者： Luan, Xinmeng Olivieri, Marco Pezzoli, Mirco Antonacci, Fabio Sarti, Augusto Politecn Milan Dipartimento Elettron Informaz & Bioingn DEIB Piazza Leonardo Da Vinci 32 I-20133 Milan Italy

ISBN: (纸本)9789464593617;9798331519773

We present a novel approach to Near-field Acoustic Holography (NAH) with the introduction of the Complex-Valued Kirchhoff-Helmholtz Convolutional neural network (CV-KHCNN). Our study focuses on analyzing Complex-Valued neural networks (CVNNs) in the application of NAH scenario. We compare the performance between CV-KHCNN and its equivalent Real-Valued neural networks (RVNNs). Moreover, different complex activation functions are evaluated for CV-KHCNN. The results emphasize the effectiveness of CVNNs in tackling NAH challenges and highlight the suitability of Cardioid as the activation function for CVNNs. This discovery underscores the promising contributions of CVNNs to the field of NAH. T-distributed Stochastic Neighbor Embedding (t-SNE) is further adopted to visualize the features of the embedding layer. The results show that even without prior knowledge of the vibrations, CV-KHCNN demonstrates the capability to distinguish between different boundary conditions (BCs) and mode shapes.

关键词： near-field acoustic holography complex-valued neural networks physics-informed neural network

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