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

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A Discrete-Time Collaborative Neurodynamic Approach to distributed Global Optimization 13

A Discrete-Time Collaborative Neurodynamic Approach to Distr...

引用

13th International conference on Intelligent Control and Information processing, ICICIP 2025

作者： Huang, Haoen Zeng, Zhigang Wang, Jun School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science Department of Data Science City University of HongKong Kowloon Hong Kong

ISBN: (纸本)9798331516147

In this paper, a discrete-time projection neural network with an adaptive step size (DPNN) is proposed for distributed global optimization. The DPNN is proven to be convergent to a Karush-Kuhn-Tucker point. Several DPNNs are utilized in a collaborative neurodynamic framework for solving distributed global optimization problem. The efficacy of the collaborative neurodynamic approach with DPNNs is demonstrated through simulation results. © 2025 IEEE.

关键词： neural network models

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State-Augmented Opportunistic Routing in Wireless Communication Systems with Graph neural networks

State-Augmented Opportunistic Routing in Wireless Communicat...

引用

2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Das, Sourajit NaderiAlizadeh, Navid Ribeiro, Alejandro University of Pennsylvania United States Duke University United States

ISBN: (纸本)9798350368741

In this study, we address the challenge of packet based information routing in large-scale wireless communication networks. We approach this scenario by framing the problem as a statistical learning problem, where each node in the network relies only on the local data. Our exploration focuses on the idea of opportunistic routing, which exploits the broadcast nature of wireless communication to select the optimal relay node and transmit information packets to the destination node via multiple relay nodes. We present a distributed optimization method based on state augmentation (SA) that aims to maximize the total information on different source nodes of the network. Our formulation of the problem deploys graph neural networks (GNNs) to perform graph convolution on the topological connections between the network nodes. Using unsupervised learning, we derive optimal routing policies for the source nodes across multiple flows from the GNN output. Numerical results show the superiority of our proposed method by comparing a GNN model trained against standard algorithms. © 2025 IEEE.

关键词： Graph neural networks Opportunistic routing State augmentation Unsupervised learning Wireless communication networks

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An Exhaustive Survey on the Methods and Applications of Graph neural networks 8th

An Exhaustive Survey on the Methods and Applications of Grap...

引用

8th International conference on Information Science, Communication and Computing

作者： Fan, Dong Cheng, Jieren Tang, Xiangyan Lu, Kai Hainan Univ Sch Comp Sci & Technol Haikou 570228 Hainan Peoples R China Hainan Blockchain Technol Engn Res Ctr Haikou 570228 Hainan Peoples R China Sch Cyberspace Secur Sch Cryptol Haikou 570228 Hainan Peoples R China Hainan Vocat Coll Polit & Law Dept Publ Safety Technol Haikou 571100 Hainan Peoples R China

ISBN: (纸本)9789819617838;9789819617814;9789819617807

The widespread use of deep learning has achieved unprecedented success. neural networks have achieved remarkable results in natural language processing, image recognition and other application fields. The key to the success of neural networks lies in its efficient processing of Euclidean space data, but the results obtained when traditional neural networks are used in non-Euclidean spaces are often not satisfactory to researchers. Graph neural network has been widely developed as a neural network that can be used in non-Euclidean space. This paper starts with the basic knowledge of graphs, introduces graph neural networks, and makes a new classification of them. Then it illustrates its application from 18 application areas. Finally, I put forward my own views on the future development and challenges of graph neural networks.

关键词： Graph neural networks Convolution Recurrent Graph Generation Euclidean non-Euclidean

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Execution Time Optimization for Pipeline Deep network Training on Multiple GPUs 33

Execution Time Optimization for Pipeline Deep Network Traini...

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33rd Euromicro International conference on Parallel, distributed, and network-Based processing, PDP 2025

作者： Wu, Bing-Jou Hong, Ding-Yong Liu, Pangfeng Wu, Jan-Jan National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (纸本)9798331524937

As neural network models become gigantic, they increasingly demand more time and memory for training. To meet these demands, advanced parallel computing techniques have become essential. Our research focuses on hybrid parallelism, an extension of pipeline parallelism. Pipeline parallelism splits the neural network into sub-networks distributed across a sequence of processing units, enabling simultaneous processing of different data segments on each device. Hybrid parallelism extends this concept by allocating multiple devices to each sub-network. Our research focuses on optimizing hybrid parallelism by improving how the model is partitioned and how computational devices are assigned. We address these issues by modeling the neural network as a directed acyclic graph of tensor operators, and then demonstrating that optimally partitioning this graph is NP-complete. Then, we propose a two-step approach. The first step is to determine a sequence of nodes. The second step is dynamic programming, which partitions the sequence to maintain balance across the assigned devices. In transforming the graph into a sequence, we explore two methods: one employs topological sorting, while the other clusters non-sequential subgraphs. We apply both methods and select the more effective one based on performance outcomes. We implement our algorithm and conduct experiments. The results show substantial enhancements in both the speed of partitioning and training throughput, with speedups reaching up to 23 in partitioning time and a 1.3 -fold increase in training throughput. © 2025 IEEE.

关键词： Deep neural networks

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Improved Xception with Local Dense Connections and Transition Layer for Facial Expression Recognition 24

Improved Xception with Local Dense Connections and Transitio...

引用

3rd International conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Zhang, Zhiping Abdul-Rahman, Shuzlina Sabri, Norlina Mohd College of Computing Informatics and Media Universiti Teknologi MARA Selangor Malaysia College of Computer and Mathematics Xinyu University Jiangxi Xinyu China Universiti Teknologi MARA Cawangan Terengganu Kuala Terengganu Malaysia

ISBN: (纸本)9798400707308

Traditional deep convolutional neural networks are used for facial expression recognition, which makes the number of neurons and parameters huge, wastes computing resources, and even causes problems such as overfitting and network degradation. Meanwhile, single-scale expression features cannot describe rich facial expression information and are not evenly distributed on face images. We proposed an improved Xception method based on the original Xception network, it retains the depth-separable convolution and residual structure, adds multi-scale convolution and local dense connection. Our proposed method achieved an accuracy rate of 89.54% on the FER2013 dataset, strengthened feature reuse, and enhanced generalization ability. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Adaptive Gradient Data Partition and Route Selection for distributed DNN Training 20th

Adaptive Gradient Data Partition and Route Selection for D...

引用

20th IFIP WG 10.3 International conference on network and Parallel Computing, NPC 2024

作者： Chai, Bo Tan, Xiaobin Yuan, Shenzhi Jia, Guangge Meng, Qiushi Wang, Weifeng Zhu, Shiyin University of Science and Technology of China Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China H3C Technologies Co. Limited Beijing China

ISBN: (纸本)9789819628636

As the scale of distributed training for Deep neural network (DNN) increases, communication has become a critical performance bottleneck in data center networks. In-network Aggregation (INA) can accelerate aggregating gradients process, the critical communication bottleneck in distributed DNN training. Due to the limited processing capacity of network switch, existing aggregation approaches have turned to a combination of INA and Parameter Server (PS) methods. However, these approaches employ a fixed partitioning of the gradient data, leading to suboptimal performance. This paper proposes GPRS, an adaptive gradient aggregation framework for distributed DNN training, which adaptively combines the line-rate processing of INA and large memory capacity of PS. We present an adaptive gradient data partition scheme, which divides the gradient data into INA and PS aggregation. We propose a heuristic algorithm that determines the aggregation route selection by assigning priority to the available network paths. To validate our approach, we build a real-world experimental testbed and implement the proposed framework. Experimental results show that it can overcome the limitations of traditional INA-only methods and reduce communication time by 15% ∼ 52% for DNN training of varying scales. © IFIP International Federation for Information processing 2025.

关键词： Data aggregation

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Mitigating DDoS attacks: A distributed blockchain-SDN secure IoT system enhanced by artificial neural networks 4th

Mitigating DDoS attacks: A distributed blockchain-SDN secure...

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4th International conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Tiwana, Pardeep Singh Kaur, Lakhvinder Kaur, Navpreet Wadhwa, Heena Kumari, Nisha Sharma, Rajeev Chandigarh Engineering College-CGC Landran Punjab Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781032911571

The introduction of the Internet of Things (IoT) has resulted in the enlargement of complex and intelligent solutions, which has led to an expansion of the variety of innovative services and processing capacity that are in line with the standards of life in the modern day. It is possible that the widespread usage of these services is to blame for the growing number of communication entities and devices to be element of the Internet of Things (IoT). This presents a substantial challenge in terms of the management of networks. Consequently, it is of the utmost importance to restructure the administration of the network that controls the Internet of Things (IoT). By virtue of its programmability and centralized characteristics, software-defined networking (SDN) facilitates the streamlining of network administration, enables network abstraction, facilitates network development, and has the capability to administrate the Internet of Things (IoT) network. However, there are still obstacles to overcome in terms of security when it comes to the Internet of Things (IoT). distributed denial of service assaults, often known as DDoS attacks, are regarded to be one of the most severe security concerns. Internet of Things (IoT) systems that are used on a global scale are susceptible to these attacks. This article examines DDoS assaults in software-defined networking and the Internet of Things. This research evaluates SDN, machine learning, and blockchain-based detection and mitigation technologies. An IoT architecture that is strong and secure has been proposed. Mult controllers and SDN were used to create this framework. Most people believe blockchain secures decentralized SDN-IoT networks well. As commonly known, artificial neural networks (ANNs) may improve risk assessment and mitigation. © 2025 the Author(s).

关键词： Risk assessment

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A Noise-Based Approach Augmented with neural Cleanse and JPEG Compression to Counter Adversarial Attacks on Image Classification Systems 33

A Noise-Based Approach Augmented with Neural Cleanse and JPE...

引用

33rd Euromicro International conference on Parallel, distributed, and network-Based processing, PDP 2025

作者： Kotenko, Igor Saenko, Igor Lauta, Oleg Vasiliev, Nikita Sadovnikov, Vladimir Laboratory of Computer Security Problems Saint-Petersburg Russia Admiral Makarov State University of Maritime and Inland Shipping Department of Telecommunications and Information Security Saint-Petersburg Russia

ISBN: (纸本)9798331524937

Adversarial attacks are now becoming quite a dangerous means of disrupting image processing systems that use machine learning methods for decision making. Therefore, developing effective countermeasures against adversarial attacks is becoming quite an important area of cybersecurity. The paper proposes a noise-based approach to countering adversarial attacks that is augmented with neural-cleanse and jpeg-compression technologies. The idea of the proposed approach is that adding noise distorts the effect of an adversarial attack, and neural cleaning and jpeg compression eliminate the consequences of such an effect. The paper examines the three most well-known types of adversarial attacks: Fast Gradient Sign Method, Zeroth Order Optimization and One Pixel Attack. These attacks manipulate input data, resulting in misclassification or incorrect predictions by exploiting high-frequency components that are undetectable to humans. The research was carried out on two datasets: MNIST-JPG and PC Parts Images. Two types of noise were used: Gaussian and Poisson. During the experiments, optimal parameters for these types of noise were found, ensuring maximum accuracy of image recognition after exposure to adversarial attacks. © 2025 IEEE.

关键词： Disinfection

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Anomaly Detection in Microservices Architecture Using Graph neural networks 33

Anomaly Detection in Microservices Architecture Using Graph ...

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33rd Euromicro International conference on Parallel, distributed, and network-Based processing, PDP 2025

作者： Osswald, Matthias Schonenberger, Timothy Cantali, Gokcan Soussi, Wissem Gur, Gurkan InIT WinterthurCH-8401 Switzerland University of Zürich UZH Communication Systems Group CSG ZürichCH-8050 Switzerland

ISBN: (纸本)9798331524937

The rise of cloud computing has transformed application development and deployment, with Kubernetes emerging as a key platform for managing containerized applications. This paper explores the use of Graph neural networks (GNNs) to detect anomalies in Kubernetes clusters and proposes GNN-based Anomaly Detection in Kubernetes (GATAKU), which is instrumental in maintaining security and performance. Our work involves integrating Cilium for detailed network monitoring and data collection, setting up a Kubernetes cluster with k3s and Traefik, and simulating attack scenarios to generate realistic data. Data preprocessing and feature engineering prepare this data for the GNN training. We present the GATAKU model's performance, highlighting metrics such as accuracy, precision, recall, and F 1 -score and compare it to baseline ML models, namely Support Vector Machine (SVM) and Random Forest (RF). Moreover, we discuss these findings and emerging challenges, including handling high-dimensional data, and explore practical implications for cybersecurity. © 2025 IEEE.

关键词： Cybersecurity

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Artificial Intelligence techniques for space experiments 33

Artificial Intelligence techniques for space experiments

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33rd Euromicro International conference on Parallel, distributed, and network-Based processing, PDP 2025

作者： Cuna, Federica Bossa, Maria Gargano, Fabio Mazziotta, Nicola Mario Betti, Pietro Istituto Nazionale di Fisica Nucleare ICSC Bari Italy Istituto Nazionale di Fisica Nucleare ICSC Napoli Italy Istituto Nazionale di Fisica Nucleare Firenze Italy

ISBN: (纸本)9798331524937

The application of advanced Artificial Intelligence (AI) techniques in astroparticle experiments represents a groundbreaking advancement in data analysis and experimental design. As space missions become increasingly complex, integrating AI technologies is essential for optimizing their performance and enhancing their scientific outcomes. This study focuses on the use of Graph neural networks (GNNs) for tracking systems and Transformers for calorimeters in space-based experiments. A significant challenge for trackers in space experiments is the high-noise environment, characterized mainly by backscattering tracks. We will present a novel approach that employs GNNs for node-level tasks, specifically aimed at classifying backscattering hits versus signal hits and reconstructing tracks. The goal for the space calorimeter experiment is to distinguish between particle types, such as electrons and protons. By capturing the dependencies within these features, Transformers can achieve robust classifications, even when the data spans thousands of channels or dimensions. By addressing these challenges, we aim to enhance the accuracy and reliability of data interpretation in astroparticle physics through the application of advanced Artificial Intelligence techniques. © 2025 IEEE.

关键词： Graph neural networks

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