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MultiVFL: A Training-Efficient Framework for Multiple Vertical Federated Learning 10

MultiVFL: A Training-Efficient Framework for Multiple Vertic...

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10th international conference on Big Data computing and Communications, BIGCOM 2024

作者： Huang, Ju Zhang, Lan Ding, Cheng Huang, Dongbo Xu, Lan School of Computer Science and Technology University of Science and Technology of China Hefei China Tencent Shanghai China

ISBN: (纸本)9798331509538

Vertical Federated Learning (VFL) is an effective paradigm which matches the enterprises' demands of leveraging more valuable features to achieve better model performance while keeping their data decentralized. Training costs, especially communication costs among parties have always been one of the major bottlenecks in VFL. Almost all existing approaches is aimed at optimizing the overhead of a single VFL task. However, there are situations where a passive party cooperates with multiple active parties to jointly train VFL models task by task, which causing the passive party incurring significant duplication of training costs. In this scenario, because these methods tailored for single VFL task cannot leverage the inter-task correlations, they might be suboptimal in terms of optimization and the model's performance may also be less satisfactory. In this article, we focus on reducing the training overhead of the passive party in multiple VFL collaborations. We utilize all local data of the passive party, including data discarded in the VFL task, for unsupervised training to accelerate the convergence speed of VFL training. Some new problems arise due to the data heterogeneity between tasks and the issue of catastrophic forgetting in models. In order to solve these issues, we propose a training-efficient algorithm named MultiVFL for multiple VFL collaborations. MultiVFL adopt parameter-level knowledge retention and fine-tuning algorithm, motivated by the sparsity of the bottom model in VFL. Through this approach, MultiVFL is able to maintain efficient training across various scenarios. We conducted experiments on two datasets containing five tasks each, and the results indicate that MultiVFL can continuously and efficiently learn tasks in multiple rounds of VFL collaboration. Compared to traditional VFL methods, MultiVFL saves about 70% of training overhead on tabular datasets and 21% on image datasets with excellent model accuracy. © 2024 IEEE.

关键词： Federated learning

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A novel adaptive variable forgetting factor RLS algorithm

A novel adaptive variable forgetting factor RLS algorithm

引用

2022 international conference on Informatics, Networking and computing, ICINC 2022

作者： Li, Kai Xiao, Jun Xie, JinFang Wu, RuiQi Wuhan University of Technology School of Mechanical and Electronic Engineering Wuhan China Hubei Institute of Measurement and Testing Technology Wuhan China

ISBN: (纸本)9798350309690

This paper investigates the variable forgetting factor least squares method and establishes a nonlinear functional relationship between the forgetting factor and the error signal based on the Sigmoid function. The algorithm overcomes the conflict problem of steady-state performance and dynamic performance of the fixed forgetting factor RLS algorithm. The forgetting factor gradually increases during the gradual convergence of the algorithm, which ensures the algorithm's steady-state performance while accelerating the algorithm's tracking speed and convergence speed. At the same time, this paper also analyzes the rules of the parameters α and β and the effects of the parameters α and β on the performance of the RLS algorithm. Finally, computer simulations are conducted, and the results are consistent with the theoretical analysis, confirming that the algorithm outperforms the traditional RLS algorithm. © 2022 IEEE.

关键词： Adaptive filtering

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RBBA: ResNet - BERT - Bahdanau Attention for Image Caption Generator 14

RBBA: ResNet - BERT - Bahdanau Attention for Image Caption G...

引用

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

作者： Hoang, Duc-Hieu Tran, Anh-Khoa Minh Dang, Duc Ngoc Tran, Phuong-Nam Dang-Ngoc, Hanh Nguyen, Cuong Tuan Ton Duc Thang University Faculty of Electrical & Electronics Eng. Ho Chi Minh City Viet Nam Ton Duc Thang University Faculty of Electrical and Electronics Ho Chi Minh City Viet Nam Fpt University Dept. of Computing Fundamental Ho Chi Minh City Viet Nam University of Technology Sydney School of Electrical and Data Engineering SydneyNSW Australia Fpt University Dept. of Information Technology Specialization Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350313277

In recent years, the topic of image caption generators has gained significant attention. Several successful projects have emerged in this field, showcasing notable advancements. Image caption generators automatically generate descriptive captions for images through the encoder and decoder mechanisms. The encoder leverages computer vision models, while the decoder utilizes natural language processing models. In this study, we aim to assess a comprehensive set of seven distinct methodologies, including six existing methods from prior research and one newly proposed. These methods are trained and evaluated with bilingual evaluation (BLEU) on the Flickr8K dataset. In our experiments, the proposed ResNet50 - BERT - Bahdanau Attention model outperforms the other models in terms of the BLEU-1 score of 0.532143 and BLEU-4 score of 0.126316. © 2023 IEEE.

关键词： BLEU Deep learning Encoder-Decoder Flickr8K Image Caption Natural language processing

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Blockchain-aided Decentralized Collaborative Automatic Modulation Classification for Next-Generation Wireless Networks 14

Blockchain-aided Decentralized Collaborative Automatic Modul...

引用

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

作者： Zainudin, Ahmad Alief, Revin Naufal Adi Paramartha Putra, Made Akter, Rubina Lee, Jae-Min Kim, Dong-Seong Kumoh National Institute of Technology Department of Electronic Engineering Gumi39177 Korea Republic of Politeknik Elektronika Negeri Surabaya Department of Electrical Engineering Surabaya Indonesia Kumoh National Institute of Technology Department of It Convergence Engineering Gumi39177 Korea Republic of Kumoh National Institute of Technology Ict Convergence Research Center Gumi39177 Korea Republic of

ISBN: (纸本)9798350313277

Automatic modulation classification (AMC) is essential to dynamic spectrum access in B5G and 6G networks for refarming the spectrum resources. However, the recent deep learning (DL)-based AMC framework has communication overhead, requires more computing resources, and poses security issues. Next-generation wireless networks allow distributed collaborative scenarios to provide ultra-reliable and low-latency communications (URLLC) services. This study proposes a blockchain-assisted decentralized collaborative AMC framework with a lightweight convolution neural network (CNN) model. The proposed system utilizes the federated learning (FL) technique to enable a privacy-preserving scheme by performing local training on edge and collaboratively producing a reliable model. A blockchain-based decentralized aggregation technique is implemented, using the Interplanetary File System (IPFS) as off-chain distributed storage for local and global models. This framework applied an improved accuracy-aware client selection mechanism (iACSM) to enhance the model performance and reduce communication overhead by selecting high-reputation authorized clients. Moreover, an ERC-20 token-based incentive mechanism has been developed to incentivize high-reputation selected clients. The proposed model measurement achieves high accuracy AMC with a low-complexity model structure in a trust-based decentralized learning scenario for constrained-resource wireless devices. © 2023 IEEE.

关键词： automatic modulation classification (AMC) blockchain-based decentralized aggregation client selection federated learning (FL) incentive mechanism lightweight convolutional neural network (CNN)

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NU SAFE Faculty: A Microsoft PowerApps Application for Health Monitoring in National University Philippines 13

NU SAFE Faculty: A Microsoft PowerApps Application for Healt...

引用

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

作者： Caballa, Vincent Kyle Correa, Charles Enzoe Itulid, Ken Zyrus Borgona, Daniel Lee James Guadana, Ryan Richard College of Computing and Information Technologies National University Manila Philippines

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

In the early stages of the COVID-19 pandemic, everyone is affected with changes in their lifestyle due to the enforcement of minimum public health standards and lockdowns. This study presents a web and mobile application with Microsoft PowerApps that will allow users in their health monitoring needs amidst the pandemic. The proposed web and mobile application will allow students and employees input their health declaration before entering the campus. The proposed system will also cover users to input their vaccination details and personal information for monitoring. Contact tracing through the mobile application is also proposed to assist the administrators in the early detection of confirmed positive cases. © 2022 IEEE.

关键词： COVID-19

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Efficient Deep Reinfocement Learning Framework in Edge computing Environments

Efficient Deep Reinfocement Learning Framework in Edge Compu...

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international conference on Information and Communication technology convergence (ICTC)

作者： Wan-Seon Lim Yeon-Hee Lee Fiscal and Economic Policy Intelligence Research Center Electronics and Telecommunications Research Institute (ETRI) Daejeon Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

In this paper, we propose a new framework for deep reinforcement learning in edge computing environments. First, we describe the application of distributed reinforcement learning techniques from previous research to edge computing environments. Based on network delay and the computational capabilities, we propose a framework that enhances the learning efficiency of deep reinforcement learning algorithms. In the proposed framework, the roles of the actors and the learner, running on the edge and cloud servers respectively, are dynamically adjusted based on available resources and network latency. The performance of the proposed framework is validated through experiments on a testbed.

关键词： Training Heuristic algorithms Deep reinforcement learning Information and communication technology Delays Computational efficiency Servers Edge computing convergence

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Three-Dimensional Trajectory Planning for Quadrotor UAV Based on Simplified PSO Algorithm 3rd

Three-Dimensional Trajectory Planning for Quadrotor UAV Base...

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Yin, Ruhui Jiang, Han Shu, Zilin Ren, Ziang Lin, Qinghao Huang, Zixin School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University Wuhan430200 China Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Nankai University Tianjin300350 China

ISBN: (纸本)9789819624676

Aiming to address the requirements of Quadrotor Unmanned Aerial Vehicle (QUAV) for conducting mission cruises in three-dimensional complex environments, this paper introduces an optimization algorithm for trajectory planning. By analyzing the motion of the QUAV, the dynamics model is established, along with the constraint model for its flight trajectory. Based on the basic Particle Swarm Optimization (PSO) algorithm, simplified Particle Swarm Optimization (sPSO) removes the particle velocity term from the evolution equation. This adjustment helps prevent slower convergence and lower accuracy in the later stages, which can occur due to the dispersion of particles caused by the particle velocity term. The trajectory is smoothed by using the Minimum Snap principle to ensure that it meets the flight constraints of the QUAV. The simulation results demonstrate that the sPSO algorithm is effective and feasible for application in QUAV three-dimensional trajectory planning. In comparison to the PSO algorithm, the trajectory is more optimal, and the convergence accuracy is higher. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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An Efficient IoMT-Based Heart Disease Prediction System Using Cuttlefish Algorithm with Cascaded LSTM 5

An Efficient IoMT-Based Heart Disease Prediction System Usin...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Kiruthiga, S. Narmadha, R.P. Walid, Md. Abul Ala Bhat, Sushma N. Gopika, S.G. Upadhye, Prajakta Vinayak School of Computing Veltech Rangarajan Dr.Sakunthala R&d Institute of Science and Technology Department of Cse Chennai India Information Technology Dr.NGP Institute of Technology Coimbatore India Department of Computer Science and Engineering Khulna9203 Bangladesh MGM's College of Engineering and Technology Biomedical Engineering Maharastra Navimumbai India Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India G H Raisoni Institute of Engineering and Technology Electronics & Telecommunication Engineering Nagpur India

ISBN: (纸本)9798350321425

Agriculture, healthcare, and manufacturing all use the Internet of Things. In recent years, smartwatches in healthcare monitoring have proliferated. This inspired the IoMT (IoT) network. Early disease identification by the IoMT reduces mortality. Clinical statistics must address cardiovascular disease prediction. The planned study would employ machine learning to identify heart disease risk factors. Several heart disease studies have shown limited accuracy. This research develops a cardiovascular disease diagnosis model using AI and IoT convergence. The model includes data collection, preliminary processing, classification, and parameter tuning. Wearables and other Internet of Things devices, including sensors, seamlessly gather data, which artificial intelligence uses to identify ailments. The recommended technique uses a Cuttlefish-based Cascaded Long Short-Term Memory (MCFA-CLSTM) model for illness diagnosis. MCFA adjusts CLSTM model 'weights' and 'bias' parameters. This improves medical data classification. MCFA improves CLSTM model diagnostics. Healthcare data verified the MCFA-CLSTM model. The MCFA-LSTM model diagnosed cardiac illness with 97.26% accuracy. Thus, advanced healthcare systems may use the MCFA-LSTM model to identify sickness. © 2023 IEEE.

关键词： Long short-term memory

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Blockchain-Empowered Space-Air-Ground Integrated Networks for Remote Internet of Things

Blockchain-Empowered Space-Air-Ground Integrated Networks fo...

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2023 IEEE/CIC international conference on Communications in China, ICCC 2023

作者： Liu, Wenchao Wang, Junyu Xing, Huijun Jin, Zhenzhen Zhang, Xuhui Shen, Yanyan Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China Southern University of Science and Technology Shenzhen China Shenzhen China Shenzhen China

ISBN: (纸本)9798350345384

With the rapid advancements in mobile communication technology, secure and efficient data transmission has become increasingly important. The space-air-ground integrated network (SAGIN) provides ubiquitous communications and stimulates the development of intelligent networks. Blockchain, as a decentral-ized distributed ledger technology, ensures the security of data transmission and makes it difficult for attackers to tamper with or steal data. This paper investigates the application of blockchain technology in SAGIN for mobile edge computing. Blockchain is utilized to ensure the security of data transmission, while unmanned aerial vehicles and satellites provide computing services for Internet of things (IoT) devices. This paper aims to solve the average system processing efficiency maximization problem that jointly considers data computation volume, latency, and energy consumption. As this problem is non-convex and difficult to solve using traditional methods, we propose a deep reinforcement learning approach to obtain the offloading strategy. Experimental results demonstrate the convergence and effectiveness of our proposed method compared to other benchmark methods. © 2023 IEEE.

关键词： Blockchain

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Review on Advanced Vehicle Recognition System: OCR and REST API Integration for Efficient Results 5

Review on Advanced Vehicle Recognition System: OCR and REST ...

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5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Rohit Kumar Saxena, Aumreesh Kumar Jhapate, Arun Shrivastava, Ritu Srivastava, Rajiv Sirt Bhopal India Csit Sirt Bhopal India Cse Sirt Bhopal India

ISBN: (纸本)9798350383522

Vehicle number plate recognition ranks first in traffic management, especially in densely populated areas like India. This paper presents a conceptual approach that integrates state-of-the-art technologies to accurately identify vehicles using video license plate scanning.[1] Powered by a Raspberry Pi and a set of advanced algorithms, the system is ideal for securing confined spaces including educational institutions and high-security roads Using the power of OpenCV, the system catches on and runs video programs to extract sensitive data. This study digs deep into the difficult areas of acceptable behavior and photo appearance, with license plate recognition being a major focus. A notable feature is the integration of Optical Character Recognition (OCR) technology via a REST API, which provides the ability to quickly and accurately extract colors from license plates REST API continuous capture of license plates creates a plate image, which provides key information from these plates.[3]. Experimental findings confirm the capabilities of the system, showing recognition rates in standard with neural network-based strategies for number plate recognition The proposed model opens up possibilities for various applications, from regulatory requirements to checking forged vehicle registrations, extending to activity-monitoring, and encouraging electronic tax collection In together with OCR and REST API integration, this review shows a remarkable walk away progressive system detection vehicle. The convergence assures increased accuracy and skill in number plate recognition, and helps the system for safer, smarter and safer transportation systems. © 2024 IEEE.

关键词： Advanced Image Processing Automated Number Plate Recognition Efficient Vehicle Identification License Plate Recognition OCR technology Optical Text Recognition REST API Integration Vehicle Detection System

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