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Secure Coded Computation for Efficient distributed Learning in Mobile IoT 18

Secure Coded Computation for Efficient Distributed Learning ...

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18th Annual IEEE international conference on Sensing, Communication, and Networking (SECON)

作者： Yang, Yilin D'Oliveira, Rafael G. L. El Rouayheb, Salim Yang, Xin Seferoglu, Hulya Chen, Yingying Rutgers State Univ New Brunswick NJ 08854 USA MIT Cambridge MA 02139 USA Univ Illinois Chicago IL USA

ISBN: (纸本)9781665441087

distributed computation plays an essential role in cloud and edge computing. Data such as images, audio, and text can be represented as matrices to facilitate efficient computation, especially in the domains of distributed machine learning, computer vision, and signal processing. Many coded computation algorithms have been proposed for big data applications to securely partition and distribute matrices to parallel worker devices. However, these proposals have yet to he adapted for mobile platforms beyond theoretical means. Mobile IoT networks can greatly benefit from secure distributed computing, however, commercial devices such as smartphones and tablets are much more limited in resources compared to platforms in data centers, requiring special design considerations. We investigate existing distribution schemes from an operational complexity and security viewpoint and study their perlbrmance in several mobile IoT networks, identifying performance bottlenecks in regards to communication and computation costs. From our findings, we propose new, scalable algorithms optimized to handle the unique constraints of mobile loT. Extensive evaluations of our proposals on publicly available image classification datasets show how distributed learning can be specially optimized to enhance runtime and battery performance on mobile loT by over 10x.

关键词： distributed computing coded computations edge device mobile IoT

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Improved Virtual Force Algorithm-Based Fuzzy Clustering in Mobile Wireless Sensor networks

Improved Virtual Force Algorithm-Based Fuzzy Clustering in M...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Jintao Yang Minghua Wang Wenbo Chen Shujing Xie School of Electrical Engineering University of South China Hengyang China

With the rapid development of wireless sensor networks(WSNs), Mobile Wireless Sensor networks (MWSNs) are receiving increased attention. MWSNs affect the performance of clustered networks, such as coverage and lifetime. In order to improve the coverage and the lifetime, this paper proposed fuzzy clustering method based on improved virtual force algorithm (IVFA-FC).IVFA-FC improves the network coverage and equalizes the distances between nodes through improved virtual force. Fuzzy clustering analyses multiple factors for selecting cluster head (CH) that change due to MWSN, so IVFA-FC uses fuzzy logic to optimize CH selection,to reduce the energy consumption of the network and to extend the network lifetime. In the experiments IVFA-FC is compared with the traditional virtual force algorithm and random wandering algorithm, IVFA-FC is effective in prolonging network lifetime,saving energy and improving coverage.

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TPTO: A Transformer-PPO based Task Offloading Solution for Edge computing Environments

TPTO: A Transformer-PPO based Task Offloading Solution for E...

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international conference on parallel and distributed Systems (ICPADS)

作者： Niloofar Gholipour Marcos Dias de Assuncao Pranav Agarwal Julien Gascon-Samson Rajkumar Buyya Dept. of Software Engineering and IT École de Technologie Supérieure Univ. of Quebec Montreal Canada CLOUDS Lab School of Computing and Information Systems The Univ. of Melbourne Australia

Emerging applications in healthcare, autonomous vehicles, and wearable assistance require interactive and low-latency data analysis services. Unfortunately, cloud-centric architectures cannot fulfill the low-latency demands of these applications, as user devices are often distant from cloud data centers. Edge computing aims to reduce the latency by enabling processing tasks to be offloaded to resources located at the network’s edge. However, determining which tasks must be offloaded to edge servers to reduce the latency of application requests is not trivial, especially if the tasks present dependencies. This paper proposes a Deep Reinforcement Learning (DRL) approach called TPTO, which leverages Transformer networks and Proximal Policy Optimization (PPO) to offload dependent tasks of IoT applications in edge computing. We consider users with various preferences, where devices can offload computation to an edge server via wireless channels. Performance evaluation results demonstrate that under fat application graphs, TPTO is more effective than state-of-the-art methods, such as Greedy, HEFT, and MRLCO, by reducing latency by 30.24%, 29.61%, and 12.41%, respectively. In addition, TPTO presents a training time approximately 2.5 times faster than an existing DRL approach.

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Single Node Acceleration of Generative Adversarial networks using HPC for Image Analytics 5

Single Node Acceleration of Generative Adversarial Networks ...

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5th international conference on Computational Intelligence and Intelligent Systems, CIIS 2022

作者： Ravikumar, Aswathy Sriraman, Harini School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9781450397612

Generative Adversarial networks (GAN) are approaches that are utilized for data augmentation, which facilitates the development of more accurate detection models for unusual or unbalanced datasets. Computer-assisted diagnostic methods may be made more reliable by using synthetic pictures generated by GAN. Generative adversarial networks are challenging to train because too unpredictable training dynamics may occur throughout the learning process, such as model collapse and vanishing gradients. For accurate and faster results the GAN network need to trained in parallel and distributed manner. We enhance the speed and precision of the Deep Convolutional Generative Adversarial networks (DCGAN) architecture by using its parallelism and executing it on High-Performance computing platforms. The effective analysis of a DCGAN in Graphic Processing Unit and Tensor Processing Unit platforms in which each layer execution pattern is analyzed. The bottleneck is identified for the GAN structure for each execution platforms. The Central Processing Unit is capable of processing neural network models, but it requires a great deal of time to do it. Graphic Processing Unit in contrast, side, are a hundred times quicker than CPUs for Neural networks, however, they are prohibitively expensive compared to CPUs. Using the systolic array structure, TPU performs well on neural networks with high batch sizes but in GAN the shift between CPU and TPU is huge so it does not perform well. © 2022 ACM.

关键词： Graphics processing unit

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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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Sequence-to-Sequence Knowledge Graph Completion Based On Gated Attention Unit

Sequence-to-Sequence Knowledge Graph Completion Based On Gat...

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international conference on parallel and distributed Systems (ICPADS)

作者： Fengge Yi Xiumei Wei Xiaojing Liu Xuesong Jiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5’s proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment.

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High Altitude Platform Station (HAPS)-Enabled parallel computing for Handoff Control in Vehicular networks

High Altitude Platform Station (HAPS)-Enabled Parallel Compu...

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IEEE international conference on Communications (ICC)

作者： Qiqi Ren Omid Abbasi Gunes Karabulut Kurt Halim Yanikomeroglu Jian Chen State Key Laboratory of Integrated Services Networks Xidian University Xi'an China Department of Systems and Computer Engineering Carleton University Ottawa ON Canada Department of Electrical Engineering Polytechnique Montréal Montréal QC Canada

distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks due to its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The proposed scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. We formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation-based methods are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner but also it improves the delay performance and maintains the delay stability.

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Revival of Fingerprint Recognition-based Biometric Systems

Revival of Fingerprint Recognition-based Biometric Systems

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international conference on parallel distributed and Grid computing (PDGC)

作者： Akshay Juneja Priyanka Kumari Monika Bharti Himanshu Jindal Girish Wadhwa Pirmohammad Khan Amity School of Engineering and Technology Amity University Punjab India Department of Electrical and Instrumentation Engineering Thapar Institute of Engieneering and Technology Punjab India Amity School of Engineering and Technology Amity University Punjab Mohali Punjab India Department of Computer Science and Engineering Parul University Vadodara Gujrat India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Fingerprint is one of the oldest and most reliable biometric technique. This paper presents different types of fingerprint recognition techniques including contactless recognition, indoor positioning, browsing research, and liveliness detection. Contactless recognition eliminates the need of physical touch and improves user comfort and hygiene by using sophisticated approaches such as convolutional neural networks (CNNs) and minutiae-based algorithms for precise identification. Indoor positioning utilizes fingerprint to recognize an individual using machine learning methods, such k-nearest neighbors (kNN) and support vector machines (SVM). It correlates radio signals with fingerprints for accurate location monitoring. Another approach called browser fingerprint recognition identifies distinct browser settings, such that monitoring of dynamic attributes is performed. Liveness detection is used to differentiate forged fingerprint from genuine records during presentation assaults using texture analysis and deep learning algorithms. This paper presents the study of recent advancements in these domains, that emphasizes the key features and limitations of existing techniques in fingerprint-based biometric systems for future researchers.

关键词： Support vector machines Machine learning algorithms Heuristic algorithms Fingerprint recognition Nearest neighbor methods Grid computing Browsers Reliability Object recognition Monitoring

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Enhanced Slicing based Deep Learning architecture for 5G networks in distributed Learning

Enhanced Slicing based Deep Learning architecture for 5G net...

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international conference on computing and Networking Technology (ICCNT)

作者： Atul Dadhich Amar Shankar Aarsi Kumari Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Food Technology School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science & IT ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper gives a distributed deep getting-to-know (DDL) architecture for 5 G community cutting. DDL is an allotted method which has been a success in schooling massive-scale deep mastering fashions. It applies the ideas of disbursed computing to reduce the dimensions of the statistics being trained at the same time as education samples in parallel on different computing assets. The proposed solution in this paper exploits the improvements in 5G networks’ virtualization skills, which include community cutting, to provide a green architecture which allows DDL fashions to be taught with dispensed resources to limit the training time. The proposed structure consists of three predominant modules: a useful resource-cutting manager, a dispensed training cluster, and a dispensed inference cluster. The resource-reducing manager allows dynamic aid cutting throughout numerous assets (e.g. GPU, CPU). The distributed schooling cluster includes workers distributing the training duties across the sources. The allotted inference cluster is used to perform inference on the trained fashions throughout more than one allotted computing source. The advantages of the proposed structure are that it allows green disbursed schooling, green disbursed inference, and value-green education of complex deep gaining knowledge of models. As such, it may be used to educate huge and complex models with decreased prices and also boost the schooling throughput. Additionally, 5G community cutting presents flexibility in useful resource provision.

关键词： Training 5G mobile communication Computational modeling Scalability Layout Graphics processing units Computer architecture Throughput Dynamic scheduling Virtualization

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Real-Time Thyroid Nodule Precise MIoT Leveraging Quad-Node Raspberry Pi Clusters and NVIDIA Jetson Nano for Edge AI

Real-Time Thyroid Nodule Precise MIoT Leveraging Quad-Node R...

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computing, Communication, Perception and Quantum Technology (CCPQT), international conference on

作者： Xian-Xian Liu Lijian Tan Jie Yang Department of Computer and Information Science University of Macau Macau SAR of China College of Intelligent Manufacturing Chongqing Industry and Trade Polytechnic Chongqing China College of Artificial Intelligence Chongqing Industry and Trade Polytechnic Chongqing China

ISBN: (数字)9798331528386

ISBN: (纸本)9798331528393

This study introduces the thyroid nodule segmentation grid search based local patch learning (GS-LPL) network as an effective IoT solution for real-time, precise thyroid cancer segmentation. Utilizing the Turing PI and NVIDIA Jetson Nano, we developed a quadruple node distributed computing cluster for real-time cancer segmentation through parallel and distributed computing. GS-LPL features a lightweight neural network, optimized for mini-supercomputers, enabling efficient model training and deployment. The NVIDIA Jetson Nano supports real-time high-definition video processing, ideal for low-powered intelligent video analytics like Network Video Recorders (NVR). Our model is also deployable on mobile devices with limited resources. Experimental results validate our model's fast, efficient, and accurate performance in thyroid nodule cancer segmentation.

关键词： Training Performance evaluation Quantum computing Thyroid cancer Computational modeling Visual analytics Neural networks Streaming media Real-time systems Thyroid

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