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FedEdge: Accelerating Edge-Assisted Federated Learning 23

FedEdge: Accelerating Edge-Assisted Federated Learning

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32nd ACM World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Federated learning (FL) has been widely acknowledged as a promising solution to training machine learning (ML) model training with privacy preservation. To reduce the traffic overheads incurred by FL systems, edge servers have been included between clients and the parameter server to aggregate clients' local models. Recent studies on this edge-assisted hierarchical FL scheme have focused on ensuring or accelerating model convergence by coping with various factors, e.g., uncertain network conditions, unreliable clients, heterogeneous compute resources, etc. This paper presents our three new discoveries of the edge-assisted hierarchical FL scheme: 1) it wastes significant time during its two-phase training rounds;2) it does not recognize or utilize model diversity when producing a global model;and 3) it is vulnerable to model poisoning attacks. To overcome these drawbacks, we propose FedEdge, a novel edge-assisted hierarchical FL scheme that accelerates model training with asynchronous local federated training and adaptive model aggregation. Extensive experiments are conducted on two widely-used public datasets. The results demonstrate that, compared with state-of-the-art FL schemes, FedEdge accelerates model convergence by 1.14 × -3.20 ×, and improves model accuracy by 2.14% - 6.63%. © 2023 ACM.

关键词： Edge computing

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FedSTGNN: A Federated Spatio-Temporal Graph Neural Network

FedSTGNN: A Federated Spatio-Temporal Graph Neural Network

引用

International Conference on Information and Communication Technology Convergence (ICTC)

作者： Heeyong Yoon Kang-Wook Chon Min-Soo Kim Department of Electrical Engineering and Computer Science DGIST Daegu Republic of Korea School of Computer Science and Engineering KOREATECH Cheonan Republic of Korea School of Computing KAIST Daejeon Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Owing to the explosive growth of the Internet of Things (IoT), there have been vast volumes of sensor-generated time series data in various locations. A lot of network usage occurs through these locations to process the sensor-generated data. To reduce network usage, the point of data processing gradually shifts from the central cloud to servers at the edge of the Internet, called edge servers. To cover the paradigm shift, there has been a challenging issue in training a neural network model, which aggregates the data generated by different locations and causes a massive amount of transmitted data across the network. Federated learning successfully resolves this issue by exchanging model parameters in the edge server through the central cloud. Meanwhile, Spatio-Temporal Graph Neural Networks (STGNNs) have gained much attention for analyzing time series data transmitted from several locations in IoT environments. Despite the increasing number of STGNN models that have been examined, the integration of STGNNs and federated learning remains underexplored. This paper presents FedSTGNN, a framework that seamlessly adapts arbitrary STGNN models to edge computing environments. The proposed method partitions the spatio-temporal graph with a mathematical definition and then trains each partitioned data with an arbitrary STGNN model on edge servers separately. Then, it aggregates all the model parameters in the central cloud with dramatically reduced network usage. Through experiments, we demonstrate that the FedSTGNN framework could train the model with a reduced amount of network communication by utilizing the advantages of edge computing and a slight loss in accuracy.

关键词： Cloud computing Adaptation models Federated learning Computational modeling Time series analysis Mathematical models Graph neural networks Servers Internet of Things Edge computing

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Comparative Analysis of Bellman-Ford and Dijkstra Algorithms in Software Defined Networking

Comparative Analysis of Bellman-Ford and Dijkstra Algorithms...

引用

Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Prabhav Ravi Tammanashastri Shinu M Rajagopal Sahen Shivadutt Bananjee Dept. of Computer Science Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetam India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

One of the key functions of the internet is packet routing of data. A routing protocol outlines the channel of communication between routers that is utilized to connect networks. Numerous routing protocols, including OSPF and RIP are used on the internet. Each protocol has a unique packet routing function. Bellman-Ford and Dijkstra are SDN routing algorithms; the former accommodates negative weights, suiting more complex network topologies, while the latter works efficiently for non-negative weights, ensuring faster convergence. RIP’s reliance on the Bellman-Ford algorithm leads to slower convergence and potential routing cycles in bigger networks. OSPF, while stronger with Dijkstra’s algorithm, can introduce high communication overhead due to maintaining complete network topology data. Bellman-Ford employs a dynamic programming approach, handling diverse network conditions, whereas Dijkstra’s greedy strategy excels in finding the shortest path in positively weighted networks. respectively.

关键词： Accuracy Heuristic algorithms Scalability Software algorithms Routing Routing protocols Iterative algorithms Topology Software defined networking Convergence

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Comparative Analysis of Fake Account Detection Using Machine Learning Algorithms

Comparative Analysis of Fake Account Detection Using Machine...

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Artificial Intelligence and Signal Processing (AISP), INDIA, International Conference on

作者： A Padmavathi K.B. Vaisshnavi Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

With the rise of Social Media platforms and new applications, the Rapid Expansion of fake accounts has become an important concern, posing threats to security, privacy and trust-worthiness. In response, this research explores the application of machine learning techniques for the detection and reduction of fake accounts. By utilizing datasets containing, user behavior patterns, network characteristics and various parameters, we explore the efficiency of various machine learning algorithms in selective genuine users from false ones. This study includes attribute manipulation, model training and evaluation techniques made for the unique challenges of fake account detection. We examine the effects of different features, such as interaction measures, language patterns and network structures on the output of detection models. Systematic testing and validation reveal effective methods for identifying fake accounts, enhancing information security and user trust on online platforms.

关键词： Training Privacy Analytical models Machine learning algorithms Accuracy Systematics Social networking (online) Machine learning Artificial neural networks Testing

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Bi-directional GRU-Based Approach for Multi-Class Text Error Identification System

Bi-directional GRU-Based Approach for Multi-Class Text Error...

引用

International Conference for Convergence of Technology (I2CT)

作者： Shaik Reeha Boda Venkata Nikith G Mani Prakash Reddy Peeta Basa Pati Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

In today's highly competitive academic and research landscape, the impression authors leave on readers is paramount to their reputation and success. Spelling errors can significantly undermine an author's credibility and negatively impact their standing within the scholarly community. To mitigate this risk, it is imperative that authors thoroughly proofread their content before submitting it to prestigious conference journals or sharing it with fellow researchers. This paper introduces a novel solution to streamline the error-checking process and empower authors to focus on other critical aspects of their projects. We propose a sophisticated text classification model, leveraging the vast capabilities of the Recurrent Neural Network (RNN) architecture, which was trained on the extensive C4 dataset sourced from the TensorFlow Library. This model exhibits exceptional proficiency in rectifying spelling mistakes across a wide range of contexts, thereby bolstering the overall quality and clarity of written work. Notably, our text classification model trained with Bi-Directional GRU method showcases a remarkable accuracy of 67% in classifying errors into four distinct categories, providing insightful feedback to authors for further improvement.

关键词： Training Recurrent neural networks Text categorization Bidirectional control Predictive models Logic gates Data models

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Efficient Meeting Insights: NLP-Enhanced Summarization of Voice and Text

Efficient Meeting Insights: NLP-Enhanced Summarization of Vo...

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International Conference on computing and Networking Technology (ICCNT)

作者： K. Sankar Brahmachari Mangaluru Hanumesh G. Anitha Balachandra Pattanaik Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai Tamil Nadu India School of Electrical Computer Engineering Wollega University Nekemte Ethiopia Africa

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In order to improve meeting efficiency in the ever-changing workplace, research has mostly focused on sophisticated text summarization techniques such as machine learning, hybrid, semantic, and graph-based methods. These approaches have shown efficient in extracting key information from lengthy talks, hence dramatically improving decision-making and knowledge retention. However, their potential has been somewhat limited by a major concentration on processing material in a single language, which ignores the complex, multilingual environment of modern global enterprises. Our most recent research project tries to overcome this gap by combining these various summarizing approaches into acomplete, language-neutral ***, to address the specific difficulty of long meetings, when extracting insights might be especially difficult for those who are not there, we propose using the Longformer encoder-decoder paradigm. This methodology excels at distilling lengthy talks into digestible summaries, regardless of the original language, ensuring that critical insights are easily accessible to anybody, hence increasing inclusion and comprehension across multinational teams.

关键词： Knowledge engineering Bridges Computational modeling Employment Decision making Semantics Text summarization Collaboration Machine learning Data mining

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Securing Images: Cryptographic Approach in Big Data Scenario

Securing Images: Cryptographic Approach in Big Data Scenario

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Kriti Priya Shah Subodh Narayan Sah Osho Kothari H.N. Vishwas Department. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Digital Image as a form of data is a critical component of the digital age. Therefore, Image Encryption and Decryption is one of the important aspect for modern communication. Image security for such digital images remain one of the important questions to be answered [14][15]. Among all the advanced Encryption and Decryption algorithm, Advanced Encryption Standard (AES) and Chaotic Map Algorithm remains the prominent approaches for image security through advanced encryption and decryption approaches. This Study undertakes these two algorithm for encryption and decryption approaches for image. AES on one hand is considered for its strong security and consistent performance whereas Chaotic Map is considered for its dynamics for encryption. In a Big Data scenario, performance efficiency, scalability, and robustness of these algorithms are critical factors due to the large volumes of image data involved. Further statistical analysis including Entropy, Mean Squared Error (MSE), Correlation Coefficient, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) are considered for both the algorithms. Additionally both the algorithms are further evaluated on the basis of encryption and decryption time and key sensitivity for the security reasons.

关键词： Sensitivity PSNR Machine learning algorithms Chaotic communication Heuristic algorithms Scalability Digital images Entropy Encryption Optimization

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Advancing Weed Detection in Agricultural Landscapes Using computer Vision 5

Advancing Weed Detection in Agricultural Landscapes Using Co...

引用

5th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2023

作者： Tan, Bei Ren Moung, Ervin Afnan Uda, Muhd. Nur Kin Teo, Kenneth Tze Angeline, Lorita School of Computer Science Taylor's University Subang Jaya Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (纸本)9798350304152

This paper explores the need for weed detection in agricultural landscapes, where weed infestations pose significant crop productivity challenges. To address this matter, computer vision techniques are utilised to create a weed detection algorithm that accurately detects weed patches inside agricultural field images. To ensure reliable model evaluation, the dataset is split into training and testing subsets before being fed into the YOLOv3 model. The YOLOv3 model performs well in terms of precision, with an average precision of 66.4% for crop regions and a notable precision of 92.5% for detecting weeds. These results highlight the potential of artificial intelligence-driven weed detection techniques to optimize crop management practices and foster sustainable agriculture in the modern era. This research advances precision agriculture by effectively addressing weed infestations, paving the way for more efficient and targeted weed control strategies, which ultimately lead to increased crop yield and improved agricultural sustainability. © 2023 IEEE.

关键词： computer vision

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Scaffolding Process-Aware Information Systems with the AKIP Platform 1

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18th International Conference on Web Information Systems and Technologies, WEBIST 2022

作者： Telemaco Neto, Ulisses Oliveira, Toacy Pillat, Raquel Alencar, Paulo Cowan, Don Melo, Glaucia David R. Cheriton School of Computer Science University of Waterloo Waterloo Canada System Engineering and Computing Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9783031430886

ISBN: (纸本)9783031430879

Business Process Automation (BPA) refers to the automation of business processes through technology, with the goal of improving efficiency, reducing errors, and increasing productivity by eliminating manual and repetitive tasks. Over the past few years, the number of BPA platforms has increased, including both open-source and proprietary solutions. However, some challenges and limitations still exist related to the adoption of these solutions, such as vendor lock-in, limited UI/UX, limited integration, outdated technology stack, and lack of support for non-process features. To address these issues, this paper presents the AKIP platform, an open-source framework for developing process-aware information systems (PAISs) using code generation techniques. AKIP generates functional process-aware web applications from BPMN business process models, making it the only known software tool capable of generating fully functional process-aware web applications. To evaluate the effectiveness of the AKIP platform, a case study was conducted in the industry and six business processes automated into a process-oriented web application were analyzed in this paper. The study showed that the AKIP platform was able to generate a functional web application while supporting the automation of business processes with different model sizes and complexities. Furthermore, this study showed that even a team of professionals with little experience was able to produce such results by using the AKIP platform. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Automation

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Safe and Secure Communication Using SSL/TLS

Safe and Secure Communication Using SSL/TLS

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Emerging Smart computing and Informatics (ESCI), Conference on

作者： Darapureddy Devendra Kumar Jerripothula Desai Mukharzee Chirla Vijay Durga Reddy Shinu M. Rajagopal Dept. of Computer Science and Engineering Amrita School Of Computing Bengaluru Amrita Vishwa Vidyapeetham India

In an ever-evolving digital environment, prioritizing user security and authentication processes is critical. It presents a comprehensive approach to creating a secure login page using SSL/TLS certificates. We focus on ensuring the confidentiality and integrity of usernames during transmission. Our system consists of a well-thought-out user interface and background architecture, where the focus is on creating a secure communication between the client and server with SSL/TLS certificates. Developed using *** and Express, the backend follows best practices for password hashing, user authentication, and secure session management. Safe coding practices are built into the HTML and JavaScript interface to reduce common web vulnerabilities. SSL/TLS certificates play a key role in protecting transmitted data, preventing unauthorized access and protecting against accidental attacks. It describes the steps to obtain, install, and configure SSL/TLS certificates on a web server and emphasizes the use of HTTPS to protect sensitive data. In addition to SSL/TLS certificates, our system includes additional security measures. These include strong password management techniques, protections against common network attacks such as SQL injection and cross-site scripting (XSS), and the implementation of HTTP Strict Transport Security (HSTS) to improve overall security. At the end of the document, the importance of regular system updates and monitoring is emphasized in combating new security threats. By following these guidelines, web developers and security professionals can help create flexible and secure login forms, ultimately contributing to a safer online environment for users.

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