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FedGSync: Jointly Optimized Weak Synchronization and Gradient Transmission for Fast Distributed Machine Learning in Heterogeneous WAN

FedGSync: Jointly Optimized Weak Synchronization and Gradien...

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2023 IEEE international conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhou, Huaman He, Yihong Zhang, Zhihao Luo, Long Yu, Hongfang Sun, Gang University of Electronic Science and Technology of China China

ISBN: (纸本)9798350337020

Due to privacy and cost reasons, distributed machine learning in Wide-Area Networks(DML-WAN) is becoming an emerging and popular collaborative learning paradigm. However, heterogeneity in computing power and data distribution among workers in different locations has a dramatic impact on training performance, including convergence speed and learning accuracy. Most of the existing works on distributed training mechanisms either focus on computing heterogeneity or data heterogeneity, and none of them can handle both well. In this paper, we propose FedGSync, a novel distributed training mechanism to improve the training performance for DML-WAN, where computing heterogeneity and data heterogeneity usually coexist. To speed up training and improve model accuracy, FedGSync clusters workers into groups according to the similarity of their data distribution and introduce group-based weak synchronization to minimize the synchronization delays waiting for slow workers and the accuracy loss by balancing the contributions of all data distributions. To preserve data privacy and improve efficiency, FedGSync only groups workers based on principal components of gradients and design an approximate grouping mechanism based on Kmeans. To further reduce synchronization time, FedGSync prioritizes packets and uses differential transmission for gradient packets between groups. Evaluation results demonstrate that FedGSync improves convergence speed and learning accuracy under the coexistence of computing heterogeneity and data heterogeneity compared with state-of-the-art distributed training mechanisms. © 2023 IEEE.

关键词： computing Heterogeneity Data Heterogeneity Distributed Machine Learning Distributed Training Mechanism Wide-Area Network

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Simulation of spot detection under the influence of boundary layers and analysis of iterative correction methods 3

Simulation of spot detection under the influence of boundary...

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3rd international computing Imaging conference, CITA 2023

作者： Zheng, Zhili Li, Yiguo Liu, Quanxi Yue, Tong Zheng, Xintao Southwest Institute of Technical Physics Chengdu610041 China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781510671409

Spot detection is of great importance for airborne laser communications and airborne laser weapons. However, due to the boundary layer created near the optical turret by aircraft flight, a wavefront distortion is generated, which ultimately affects the imaging quality. In this paper, the flow field near an optical turret with a planar window is simulated at high speed using a numerical method. The wavefront aberrations are obtained by ray tracing and the spot images are compared for different flight altitudes, flight speeds and planar window orientations. Further, the convergence speed and correction effect of wavefront sensorless adaptive optics(WFSless AO) with different iterative control algorithms are compared. The simulation results show that the accelerated stochastic parallel gradient descent(ASPGD) algorithm is well suited for the correction of wavefront aberrations caused by boundary layer effects. © 2023 SPIE.

关键词： Adaptive optics

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Simplifying Graph-Parallel Computation in Apache Spark with GraphX 8

Simplifying Graph-Parallel Computation in Apache Spark with ...

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8th international conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Vyakaranam, Lasya Raman, Ramakrishnan Shah, Harshal Mishra, Nilamadhab Meenakshi, R. Murugan, S. Pune Maharashtra Pune India Maharashtra Pune India Parul Institute of Engineering and Technology Parul University Faculty of Engineering and Technology Gujarat Vadodara India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350367904

Streamlining Apache graph-parallel computing the goal of integrating Spark with GraphX is to make processing massive amounts of graph data easier and faster. The goal is to streamline the process of creating and running graph algorithms by using GraphX, a Spark-based distributed graph processing platform. The eventual objective is to create a scalable platform that is easy to use for graph-structured data analysis and manipulation. Improving GraphX's performance, expanding its features, and integrating it with Spark's ecosystem are all part of this process. The expected result is a flexible and strong framework that lets data scientists and developers execute complicated graph analytics tasks with ease. This will pave the way for applications in many fields, including recommendation systems, social network analysis, and biological network analysis. Optimizing performance in graph-parallel computer situations, making the development process easier, and making it more scalable are the main goals. The graph structure visualization results in graph data revealing values in the range of 1.2 to 5.6 for a sample of five distinct vertex/edge types. The degree distribution histogram in the same dataset shows that, for five different frequencies, the numbers range from around 1.5 to 4.5. For the same dataset, the PageRank convergence Plot shows values ranging from 0.4 to 0.9 for five distinct iterations/scores. © 2024 IEEE.

关键词： Apache Spark Big Data Graph Analytics Graph-Parallel Computation GraphX

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Delay Optimization for Augmented Reality Service using Mobile Edge computing Federation System 14

Delay Optimization for Augmented Reality Service using Mobil...

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14th international conference on Information and Communication technology convergence, ICTC 2023

作者： Do, Huong Mai Yoo, Myungsik Soongsil University Department of Information Communication Convergence Seoul Korea Republic of Soongsil University School of Electronic Engineering Seoul Korea Republic of

ISBN: (纸本)9798350313277

In the last year, Augmented Reality (AR) applications have used computationally intensive vision algorithms on devices with low resources. Computation offloading has shown to be an effective solution for resource constraints. We propose an AR sub-tasks offloading framework based on the AR service's sub-tasks dependency model to reduce the end-to-end latency in the MEC federation system while taking resource situation into account. The problem is then solved by switching to the Markov Decision Process (MDP) and employing the Deep Deterministic Policy Gradient (DDPG) model. The simulation findings suggest that the DDPG can successfully reduce the AR task latency. © 2023 IEEE.

关键词： Augmented Reality deep reinforcement learning (DRL) Markov decision process MEC federation resource allocation task of-floading

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Research on the Application of Thinking Neural Signal Recognition and Classification in Personalized Art computing Library 3

Research on the Application of Thinking Neural Signal Recogn...

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3rd IEEE international conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Xiaosu, Su Hubei University of Technology Wuhan China

ISBN: (纸本)9798350380989

Electroencephalogram (EEG) is a key resource for the study of brain function, and the interaction between computer and machine through EEG has become a new computer interaction mode. This project intends to study the EEG - computer interaction model based on facial expression. A neural cluster model based on neural network was used to model the mechanism of facial emotions-induced EEG. The EEG activity induced by facial emotion and its spectral characteristics were obtained. In order to improve the recognition accuracy of emotion-driven EEG, the analysis and recognition algorithm of EEG stimulated by expression is studied by combining wavelet with ANN model. The simulation results show that the correct rate of EEG recognition based on neural network is 88.69%. The recognition accuracy of BP neural network is 76.3%, and its convergence speed is also improved. The experimental results show that this method is an effective method for EEG recognition. © 2024 IEEE.

关键词： Brain-computer interface electroencephalogram feature extraction neural network

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Studies on Artificial Intelligence technology's Use in Computer Network Upkeep and Operation 3

Studies on Artificial Intelligence Technology's Use in Compu...

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3rd international conference on Intelligent computing, Communication and convergence, ICI3C 2023

作者： Zhu, Hao Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350343922

The study of Artificial Intelligence Techniques in Computer Network Operations and Maintenance aims to explore how advanced AI methods and techniques can be utilised to improve network performance, efficiency of management and maintenance, and reliability. This area of research is of increasing interest as the complexity and size of networks continues to grow, requiring smarter, automated solutions to meet changing demands. Artificial intelligence techniques can be used to monitor network performance metrics in real time and automatically detect and alert on abnormal events. Machine learning and time series analysis methods are widely used to detect network performance anomalies, thus helping operations staff identify and resolve problems more quickly. Artificial intelligence techniques can be used to automate network configuration, troubleshooting and resource optimisation. Automated network management systems can dynamically adjust network parameters according to real-time demand, improving network adaptability and flexibility. Using machine learning and data mining technologies, network equipment failures or performance degradation can be predicted so that maintenance measures can be taken in advance to reduce unnecessary downtime. Artificial intelligence technology also plays an important role in the field of network security. It can be used to detect network intrusion, threat analysis and malware detection to improve network security and protect user data. In conclusion, the use of AI technology in computer network operations and maintenance provides more intelligent and automated tools for network management and maintenance, helping to improve network performance, reduce failures and improve security. Ongoing developments in this area will continue to drive innovation in network operations and maintenance, providing more reliable network services to a variety of industries and organisations. © 2023 IEEE.

关键词： Network intrusion

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Fractional Order Ultra-Local Model-Based Sliding-Mode-Assissted Disturbance Observer and Model-Free Control

Fractional Order Ultra-Local Model-Based Sliding-Mode-Assiss...

引用

2024 international conference Automatics and Informatics, ICAI 2024

作者： Zhang, Liuchang Wang, Haoping Tian, Yang Zhuang, Xincheng Azar, Ahmad Taher Ahmed, Saim Nanjing University of Science and Technology Dept. School of Automation Nanjing China Prince Sultan University Dept. Automated Systems & Soft Computing Lab Riyadh Saudi Arabia Prince Sultan University Dept. College of Computer and Information Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798350353907

This paper puts forward a fractional order sliding-mode-assisted disturbance observer-based model-free control (FO-SAMDOMFC) methodology for fractional order system with disturbance. First, the system dynamic model is rewritten as a fractional order ultra-local model (ULM), and a SMADO is designed to estimate the lumped disturbance term. Then, A closed-loop control law is proposed on the basis of the ULM with the objective of guaranteeing the convergence of the tracking error. Next, a proof of the stability of the closed-loop system is given based on the fractional extension of Lyapunov's direct method. Finally, the simulation compared with other controllers based on MATLAB/Simulink is realized, and the simulation results validate the proposed method. © 2024 IEEE.

关键词： Sliding mode control

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Combined Cloud and Deep Learning Models for Enhanced Decision Support 4

Combined Cloud and Deep Learning Models for Enhanced Decisio...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Vyshnavi, A. Borade, Monali Ravindra Kannimuthu, S. Singla, Abhishek Raajini, X. Mercilin Rampal, Sourav School of Mangement - Ug Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam College of Engineering Department of Information Technology Coimbatore641032 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798331518981

Cloud computing and deep learning are changing decision-support systems. This combination was driven by cloud computing and deep learning models' complimentary data processing capabilities. Traditional decision-support systems typically fail due to the complexity and variety of current information, particularly unstructured and semi-structured data. Innovative methods are required to overcome this restriction and exploit big data's immense potential. Cloud computing is essential for handling and storing huge amounts of data because to its design, scalability, and storage capacity. Company data is securely accessed, processed, and stored while being cost-effective and flexible. Deep learning models - a subset of AI - offer a great toolbox of neural networks and algorithms for analyzing complex data patterns. These models are versatile, train well, and can find patterns in unstructured data. However, ideal outcomes demand a lot of processing power. Deep learning and cloud computing operate together better than traditional decision-support systems. Instead of static, rule-based models, the decision-support system may now react to complicated, ever-changing data. Modern cloud-based algorithms enable organizations to make accurate, personalized choices in real time. These changes will affect healthcare, finance, manufacturing, and autonomous automobiles. convergence causes actual concerns. Data security, privacy, and equality are key concerns. Cloud systems are difficult to balance between usability and safety. Since deep learning algorithms may be prejudiced and provide unfair outcomes, we must continue to promote fairness and transparency. Additional challenges to the effective implementation of these converged systems include controlling scalability, ensuring compliance with laws, and addressing interoperability. © 2024 IEEE.

关键词： Artificial intelligence Big data Cloud computing Data analysis Data privacy Data processing Decision support Deep learning Machine learning Scalability

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Experimental Analysis of Learning Based Approach to Evaluate the Integration of Social Networking Platform with Internet of Things technology

Experimental Analysis of Learning Based Approach to Evaluate...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Karunakaran, A. Venkatraman, C.B. Suganthi, P. Santhi, G.B. Alok, Narkhede Shalout, Imad S. A. Engineering College Department of Ece Chennai India Sona College of Technology Department of Eee Salem India Sri Sai Ram Institute of Technology Department of Cse Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa university Faculty of engineering Department of Civil Engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Enterprising entrepreneurs are forging a new paradigm for consumer-business interactions by leveraging the convergence of social networking platforms-such as Facebook and Twitter-and the Internet of Things (IoT). This paper aims to assess an integration pipeline's performance using a learning based approach. Our aim is to understand the complex interplay between data that has been collected from IoT and how social media users act using advanced methods in data analytics. Our approach involves gathering data from both SNPs and the Internet of Things (IoT) and pre-processing it as needed before building a model that can be predicted and its performance evaluated. As opposed to the proposed method Elevated Social Networking Integration (ESNI) is a better strategy than the existing Social Networking Integration Analyzer (SNIA), which permits collecting less information through connected devices to enhance decision-making on consumer behavior and encounters. By offering a methodical approach to evaluating the value proposition of combining IoT with SNP, this study adds to the corpus of knowledge. The framework facilitates seamless interaction across diverse social media platforms, optimizing data flow and user experience. Empirical testing demonstrates ESNI's effectiveness in boosting engagement metrics and user satisfaction compared to conventional integration methods. This research highlights ESNI's potential to redefine social networking strategies, providing a robust foundation for future innovations in digital communication and marketing. © 2024 IEEE.

关键词： Data assimilation

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A Method of PV Power Generation Forecasting using Constrained Transformer Network 5

A Method of PV Power Generation Forecasting using Constraine...

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5th international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Tran, Duc Hoang Nguyen, Van Linh Utama, Ida Bagus Krishna Yoga Nguyen, Huy Chung, ByungDeok Jang, Yeong Min Kookmin Univ Dept Elect Engn Seoul 02707 South Korea ENS Co Ltd Ansan 15655 South Korea

ISBN: (纸本)9781665456456

This paper introduces a method using a physics-constrained Transformer model attached to an edge computer for photovoltaic power generation that considers specific domain knowledge and increases the performance of the system. It attempts to address the drawback of current machine learning algorithms, which are applied based simply on enormous amounts of data, readily creating projections that are irrational. The experimental results indicate that the proposed physics-constrained Transformer model attached to an edge computer for photovoltaic power generation Transformer model possesses stronger prediction capability than other methods and is completely suitable with the sparse data in practice.

关键词： time-series forecasting transformer neural network edge computing

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