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Federated Learning with Class Balanced Loss Optimized by Implicit Stochastic Gradient Descent 7th

Federated Learning with Class Balanced Loss Optimized by Imp...

引用

7th international conference on Soft computing in Data Science (SCDS)

作者： Zhou, Jincheng Zheng, Maoxing Qiannan Normal Univ Nationalities Sch Comp & Informat Duyun 558000 Peoples R China Baoji Univ Arts & Sci Sch Comp Sci Baoji 721007 Peoples R China Key Lab Complex Syst & Intelligent Optimizat Guiz Duyun 558000 Peoples R China

ISBN: (纸本)9789819904044;9789819904051

Federated learning is a paradigm for distributed machine learning in which a central server interacts with a large number of remote devices to create the optimal global model. System and data heterogeneity are now the two largest impediments to federated learning. This work suggests a federated learning strategy based on stochastic gradient descent optimization as a solution to the problem of heterogeneity-induced slow convergence, or even non-convergence, of the global model. This work estimates the average global gradient using locally uploaded model parameters without computing the first derivative or updating global model parameters through gradient descent. Allowing the global model to be used with fewer communication rounds. Obtain faster and more reliable convergence results. In experiments simulating varying degrees of heterogeneous settings, the strategy proposed in this work delivered faster and more stable convergence than FedProx and FedAvg. This work offers a strategy that decreases the number of communication cycles on highly heterogeneous synthetic datasets by around 50% compared to FedProx, therefore considerably enhancing the stability and durability of federated learning.

关键词： Fast convergence Optimization algorithm System heterogeneity Data heterogeneity Implicit stochastic gradient descent Global model Central server Distributed machine learning Federated learning

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Optimization of Fair Materials Distribution Considering the Integrated Road Network 24

Optimization of Fair Materials Distribution Considering the ...

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3rd Asia conference on Algorithms, computing and Machine Learning, CACML 2024

作者： Liu, Fang Liang, Tao Li, Feng Xia, Qiang Ma, Fangfang Beijing Jinghang Research Institute of Computing and Communication Beijing China Academy of System Engineering Academy of Military Sciences Beijing100166 China Beijing Institute of Surveying and Mapping Beijing Key Laboratory of Urban Spatial Information Engineering Beijing100074 China

ISBN: (纸本)9798400716416

To address the issue of balancing timeliness and fairness in the distribution process of materials, a road network is constructed and an improved proportional sharing comprehensive cost variable neighborhood ant colony algorithm is proposed. A comparative analysis of distribution based on traditional ant colony algorithm and improved algorithm under the road network is designed. The analysis shows that the constructed road network is closer to the real path than a single road network. Secondly, the improved algorithm reduces the number of convergence iterations and improves operational efficiency. Thirdly, adding population weight and spatial clustering grouping can obtain a more objective spatial division to achieve fair distribution. The research results can be used for road network planning in complex situations, especially for quickly developing disaster relief plans and transportation support in regional wars or emergencies. It can provide a theoretical reference for the planning of road networks in complex situations. © 2024 ACM.

关键词： Ant colony optimization

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A Survey on Mobile Edge computing Architectures for Deep Learning Models 13

A Survey on Mobile Edge Computing Architectures for Deep Lea...

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13th international conference on Information and Communication technology convergence, ICTC 2022

作者： Lee, Jaehwan Na, Woongsoo Kongju National University Department of Computer Science and Engineering Korea Republic of Kongju National University Department of Software Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

With the development of mobile devices and communication technology, the IoT paradigm has recently been gaining attention. Various sensors and high-speed communication technology are equipped by various mobile devices, making it possible to collect a large amount of environmental data. Accordingly, many attempts have been made to process data collected from mobile devices into deep learning models. Since mobile devices have relatively insufficient computing resources and battery constrains to directly execute large-scale deep learning models, these tasks are offloaded to a cloud computing platform instead. In addition, mobile edge computing (MEC) or fog computing architecture has been proposed in order for the heavy tasks to be processed in a nearby computing node, thereby address the latency limits of cloud computing architecture. The deep learning inferencing can be performed with low latency on the surrounding edge computing nodes by applying the MEC paradigm, however, finding the optimal offloading node selection has been challenging problem, which triggered various studies have been conducted. In this paper, communication technologies and edge computing architectures for offloading deep learning models are examined and comparatively analyzed. © 2022 IEEE.

关键词： Computer architecture

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Iterative Physics-Informed Neural Network for Solving Coupled Partial Differential Equations 3

Iterative Physics-Informed Neural Network for Solving Couple...

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3rd international conference on Cloud computing, Big Data Application and Software Engineering, CBASE 2024

作者： Zhou, Chen Min, Qiang Wang, Qimin Li, Chao Li, Yunpeng School of Mechanics and Aerospace Engineering Dalian University of Technology Dalian China Chengdu Aircraft Design & Research Institute AVIC National Key Laboratory of Aircraft Digital Agile Design Chengdu China Numerical Simulation Research Institute Luoyang SiPESC Ltd. Luoyang China

ISBN: (纸本)9798331506582

The numerical solution of coupled partial differential equations (PDEs) represents a significant challenge for traditional methods such as the finite element method (FEM), particularly in the context of numerical stability and mesh generation. In this paper, an iterative physics-informed neural network (PINN) for solving coupled PDEs is proposed. The coupled PDEs are decomposed into a number of subproblems, which are then solved using multiple PINNs. An iterative method is proposed to update PINNs and reach convergence with coupled PDEs. The proposed method is capable of efficiently solving complex systems without the generation of fine meshes in the absence of data. The effectiveness and accuracy of the method are verified through an example of a coupled fluid dynamics and heat transfer example. © 2024 IEEE.

关键词： Fluid dynamics

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Temperature compensation for humidity sensors using ISSA-BP neural network 30

Temperature compensation for humidity sensors using ISSA-BP ...

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30th international conference on Mobile computing and Networking, ACM MobiCom 2024

作者： Li, Dapeng Zhang, Hechu Yang, Yu Li, Wei Wang, Shuai Zheng, Dezhi School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China School of Electronics and Information Engineering Beihang University Beijing China Department of Mechanical Engineering University College London London United Kingdom School of Computer Science and Engineering Beihang University Beijing China Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing China

ISBN: (纸本)9798400704895

High-precision humidity detection is essential in various fields. However, the sensor's output signal is often affected by complex environmental temperature changes. To address these challenges, this paper propose an improved sparrow search algorithm based on the back propagation neural network (ISSA-BP). This method optimizes the initialization process of the traditional algorithm and introduces an edge evolution strategy to enhance its iterative update process, significantly improving both efficiency and accuracy. Experimental results demonstrate that the proposed algorithm reduces the mean absolute percentage error (MAPE) from 4.35% to 0.92% and decreases the convergence time from 1.25 s to 0.65 s, compared to traditional methods. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Humidity sensors

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Influencing Factors Analysis of Regional Industrial Economy Based on Neural Network and Ridge Regression Model 16

Influencing Factors Analysis of Regional Industrial Economy ...

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16th th international conference on Intelligent computing technology and Automation, ICICTA 2023

作者： Liu, Zhangwen Wenzhou Vocational College of Science & Technology Wenzhou325000 China

ISBN: (纸本)9781643684864

To obtain the important variables affecting industrial competitive advantage, an analysis model of influencing factors based on multiple regression linear is constructed. Taking the impact of RCEP on local economy in Wenzhou as an example, the scheme makes an empirical analysis on the important factors of economic growth of the industry. The index system of economic influencing factors is established by the principle of time series prediction. Then, ridge plot is used to evaluate independent variables and use the results as input variables for BP training network to achieve more stable prediction accuracy. According to the research results, we preliminarily judge the impact of various factors on the region. The combination of two models if verified to have better convergence speed and prediction accuracy, which also provides the countermeasures and suggestions for the development of industrial economy. © 2024 The Authors.

关键词： Multiple linear regression

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Mitigating Broadcast Propagation in Redundant BMA Networks Using Layer 3 Uplinks

Mitigating Broadcast Propagation in Redundant BMA Networks U...

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2024 IEEE international conference on Communications, computing, Cybersecurity and Informatics, CCCI 2024

作者： Thankgod-Ibulubo, Boma Singh, Harpreet Ifeanyi Nwakanma, Cosmas Allotey-Acquaye, Edwin University of Hertfordshire Department of Computer Science Hatfield United Kingdom Kumoh National Institute of Technology ICT-Convergence Research Center Gumi Korea Republic of

ISBN: (纸本)9798350349832

The purpose of redundancy in computer networks is to increase the robustness and resilience of the network. Deploying multiple paths to network devices or redundant devices could accomplish this purpose. It would also increase the complexity and chances of having a broadcast storm within the network due to potential loops from multiple paths. The Spanning-Tree Protocol was developed to uphold these desired network features and solve the loop problem. Layer 3 switches have facilitated the deployment of layer 3 links between specific switches which was only possible with layer 2 links. This paper explores alter-native layer 3 (Open Shortest Path First and Enhanced Interior Gateway Protocol) protocols that could efficiently replace the spanning-Tree protocol in Broadcast Multi-Access Networks. An analysis of the performance of these protocols was made in com-parison to spanning-Tree in their ability to uphold redundancy, resilience and robustness. Experimental tests were implemented for User Datagram Protocol and Transmission Control Protocol transmissions. The results showed that both layer 3 protocols can efficiently replace spanning-Tree in Broadcast Multi-Access Networks. © 2024 IEEE.

关键词： Gateways (computer networks)

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Performance Evaluation of Object Detection Considering C-V2X Communication Errors 14

Performance Evaluation of Object Detection Considering C-V2X...

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

作者： Kim, Jeeyoo Choi, Ji-Woong Kim, Donghyeon Choi, Sinuk Daegu Korea Republic of

ISBN: (纸本)9798350313277

To cope with the issue of high computational loads in vehicles equipped with multiple sensors for safe autonomous driving, vehicular edge computing (VEC) has been proposed. When applying VEC, it is important to analyze the recognition performance considering communication errors because data quality can be degraded due to communication errors, which can result in decreased recognition or processing performance. Previous studies have mainly focused on aspects such as delay and cost. However, there is a need for analysis specifically addressing recognition performance in light of communication errors. This paper suggests the optimal sensor resolution to enhance object detection performance based on the signal-to-noise ratio (SNR). We analyze the performance of each resolution according to SNR. Based on these results, we anticipate that selecting the optimal resolution for object detection based on channel conditions during offloading can further improve driving safety. © 2023 IEEE.

关键词： autonomous driving object detection offloading Vehicle-to-everything (V2X) vehicular edge computing (VEC)

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Accelerating Column Generation Algorithm Using Machine-Learning-Based Column Elimination

Accelerating Column Generation Algorithm Using Machine-Learn...

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

作者： Fang, Lichang Yuan, Haofeng Zhang, Yuli Song, Shiji Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China BNRist Tsinghua University Department of Automation Beijing China School of Management and Economics Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350337020

The column generation (CG) algorithm is widely used in large-scale optimization problems. However, a large amount of columns in the restricted master problem (RMP) makes the computing process very time-consuming. This paper proposes a machine learning based column elimination strategy to accelerate the CG algorithm. Our approach represents the RMP by a bipartite graph and applies a learned Graph Neural Network model to predict redundant columns to be eliminated from the RMP, so as to reduce the time cost of solving the RMP and iterations required for convergence. Our approach is tested on cutting stock problem instances. Compared with the vanilla CG algorithm, the iterations and time required for convergence are reduced by up to 31 % and 48%, respectively. Furthermore, our approach shows great generalization to cutting stock problem instances of different sizes. © 2023 IEEE.

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Predicting Financial Success: Regression Modeling of Sustainable Business Practices in Start-up Ecosystem 18

Predicting Financial Success: Regression Modeling of Sustain...

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18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Sharma, Riya Christa, Sharon Mani, Ambica Prakash Sarishma Graphic Era Deemed to Be University Department of Commerce Dehradun India Mit Art Design and Technology University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9789380544519

This research investigates the intricate relationship between sustainability adoption and the financial performance of startups in the Indian business landscape. Utilizing data from a survey conducted among 152 startups, the study employs robust statistical analyses, including regression to discern the impact of sustainable practices on financial outcomes. Positioned at the nexus of global imperatives for sustainable development and the pragmatic challenges faced by startups, the research addresses the unique context of India's entrepreneurial ecosystem. By combining empirical insights with statistical rigor, the study illuminates the mechanisms by which sustainability affects financial health. This study provides actionable information for startup decision-makers, informs policy discussions, and broadens scholarly discourse on the symbiotic relationship between sustainability and financial performance. Its findings are relevant to a wide range of stakeholders, providing a nuanced knowledge of how entrepreneurs might navigate the convergence of sustainability and economic resilience within the unique contours of the entrepreneurial scene. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： circular economy startup sustainable business model sustainable business practices triple bottom line

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