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Federated Learning: Achieving Scalable and Privacy-Preserving Machine Learning

Federated Learning: Achieving Scalable and Privacy-Preservin...

引用

International conference on image Information processing

作者： Harsh Bansal Kanu Goel Department of Computer Science and Engineering Amity University Punjab Mohali India Department of Computer Science and Engineering Punjab Engineering College (Deemed to be University) Chandigarh India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Federated Learning is a machine learning framework in which multiple clients engage in collaborative training of a model, while the training data remains distributed across the client devices. The training process is overseen by a centralised server. The utilisation of this approach effectively reduces numerous privacy risks and computation costs commonly associated with conventional, centralised machine learning techniques, wherein user data is transmitted to a central server for processing. This research paper is motivated by the increasing interest in the field of Federated Learning research, and aims to explore the advantages of this approach in comparison to conventional methods. The article additionally examines the obstacles and potential uses of Federated Learning, as this nascent framework garners increasing attention.

关键词： Training Data privacy Costs Federated learning Training data Information processing Data models

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A Referenced Framework on New Challenges and Cutting-Edge Research Trends for Big-Data processing Using Machine Learning Approaches

A Referenced Framework on New Challenges and Cutting-Edge Re...

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Smart Computing and Application (ICSCA), International conference on

作者： Madhu Kirola Minakshi Memoria Mohammed Shuaib Kapil Joshi Shadab Alam Faisal Alshanketi Department of Computer Engineering Uttaranchal University Dehradun India College of Computer Science & IT Jazan University Jazan Saudi Arabia

Machine Learning and Big- Data Analytics is each growing computing fields. Big data is, in reality, gaining attraction in all regions of Science and engineering. While those big-data units have the clean capacity, successfully comprehending them will call for new thinking methods and inventive learning methodologies to cope with the severe issues. On the other hand, traditional machine learning strategies may not be able to fulfil the needs of real-time data processing for big -datasets because of the big-data age approaches. As a result, machine learning will reinvent itself for the big-data era. Where improvements in any discipline Big- Data analytics or machine Learning complement the opposite. This article offers a literature evaluation of the most recent advancement in ML (machine learning) approaches for large amounts of data processing. We focused on evaluating and analyzing the challenging situation and potential solutions associated with handling huge data sets with machine learning. This review article initially presents an overview of big-data and big-data analytics, followed by reviewing traditional machine-learning approaches as well as examining new aspects of machine-learning techniques for big-data processing. After that identifying the critical challenges of big-data processing correlated with the properties of big data 5Vs-volume, velocity, variety, veracity, and value, finally proposed a reference framework for big-data processing along with new emerging tools and technologies like Hadoop, distributed computing, and parallel computing along with the identification of open issues and new research trends associated with the processing of large amounts of data sets (Big-Data).

关键词： Data analysis Decision making distributed databases Machine learning Transforms Big Data parallel processing

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Controllable Template Generation for Document-level Event Extraction

Controllable Template Generation for Document-level Event Ex...

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Neural Networks, Information and Communication Engineering (NNICE), International conference on

作者： Quntian Fang Feng Liu Zhen Huang Zhenliang Guo Changjian Wang Dongsheng Li Minghao Hu National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China Information Research Center Academy of Military Sciences Beijing China

Document-level event extraction task has achieved significant progress based on template generation methods. However, there is no reasonable regulation and restriction in the existing template-based generation methods, which results in the uncontrollability of the generation results. In some scenarios, model generates entities that do not belong to the input text, or generate template content repeatedly. It is determined by the nature of the extraction task and the generation task. To this end, we propose a controllable template generation event extraction model. According to the characteristics of template generation and event extraction tasks, the model devises copy mechanism, inhibition mechanism and rejection mechanism under the appropriately constructed template. Our model achieves state-of-the-art result on MUC-4 dataset, and finally through experimental analysis, it demonstrates the effectiveness of each mechanism we proposed.

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Plant Leaf Classification Based on Efficient Attention Mechanism ECSA

Plant Leaf Classification Based on Efficient Attention Mecha...

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Data Intelligence and Innovative Application (DIIA), IEEE International conference on

作者： Lei Peng Xiao Qin Wenji Wang Yunqin Fu Sijing Tan Yongyu Li Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning Guangxi China

ISBN: (数字)9798331504434

ISBN: (纸本)9798331504441

In this study, we proposed an Efficient Channel and Spatial Attention module (ECSA), which aims to improve the performance of convolutional neural networks in plant leaf image classification tasks by combining spatial and channel attention. The ECSA module can make full use of attention information while keeping the model lightweight, and enhance the model's recognition ability of fine grain characteristics of plant leaves. Experiments on the self-made small plant leaf dataset and the public dataset Cifar-10 prove that ECSA module has better performance than other mainstream attention modules in image classification tasks. In addition, the ablation experiment further analyzed the effects of channel attention and spatial attention on the model performance in different dimensions, as well as the effects of serial and parallel information acquisition methods. It was found that channel attention had the most significant improvement on the model performance, and the serial information acquisition method was slightly better than the parallel method. At the same time, the influence of cross-information interaction region size on the model performance was also discussed, and the optimal region size on the plant leaf dataset was determined. Overall, the ECSA module shows remarkable potential in processing fine-grained plant leaf image classification problems, and has good generalization ability.

关键词： Analytical models image recognition Attention mechanisms Accuracy Convolutional neural networks Character recognition image classification

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Knowledge Graph Completion Based on Contrastive Learning for Diet Therapy

Knowledge Graph Completion Based on Contrastive Learning for...

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ACIS International conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Kaidi Yang Yangguang Lin Xuanhan Mi Yuxun Li Xiao Lin Dongmei Li Center for Forestry-oriented Intelligent Information Processing National Forestry Beijing Forestry University Engineering Research Beijing China

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

In recent years, an increasing number of individuals have turned to traditional Chinese medicine diet therapy as a means to nourish their bodies and mitigate diseases. With the advent of the big data era, knowledge graphs, as powerful analysis tools, can provide more accurate and personalized dietary advice for diet therapy. However, most of the current diet therapy knowledge graphs have imperfections. To address this issue, we construct a diet therapy knowledge graph by utilizing textual data and professional books provided by the Academy of Traditional Chinese Medicine, from which we extract entities and relations. Building upon this foundation, we introduce a text representation technique predicated on contrastive learning, designed to augment the semantic richness of the knowledge graph and enhance the completion of the diet therapy knowledge graph. By conducting experiments on the diet therapy knowledge graph and public datasets, the results show that our method can capture the semantic information in the knowledge graph more efficiently compared to traditional methods. This provides new possibilities for research and practice in the field of traditional Chinese medicine diet therapy. This research opens new avenues for leveraging big data analysis in traditional Chinese medicine diet therapy.

关键词： Training Accuracy Semantics Software algorithms Medical treatment Knowledge graphs Contrastive learning

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An Efficient Reduced-Memory GPU-based Dynamic Programming Strategy for Bounded Knapsack Problems

An Efficient Reduced-Memory GPU-based Dynamic Programming St...

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International conference on parallel distributed and Grid Computing (PDGC)

作者： Girish Biswas Nandini Mukherjee Department of Computer Science and Engineering Jadavpur University Kolkata India

ISBN: (纸本)9781665454025

Many dynamic programming approaches are existing for 1-0 Knapsack problem (KP) for fast GPU-based solution. These dynamic programming methods can be used for solving the problem of Bounded Knapsack Problem (BKP) after converting it into an equivalent 1-0 Knapsack problem. But, after conversion, large problem of BKP becomes too large 1-0 KP to fathoming via these methods on GPU-based system because of limited space availability in GPU device. In this paper, we present an implementation of an efficient dynamic programming technique to solve BKP on GPU-based system via CUDA. With an aspect to preserving the original problem size, BKP is not required to convert into equivalent 1/0 knapsack problem in this approach. Large problems are carried out with only minimally requisite CPU-GPU interactions and less memory occupancy on GPU for better efficiency and memory utilization. This GPU oriented parallel procedure shows brilliant speedup over sequential implementation of the dynamic technique.

关键词： Upper bound Multicore processing Memory management Graphics processing units Programming parallel processing Grid computing

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Hyperdimensional computing as a framework for systematic aggregation of image descriptors

Hyperdimensional computing as a framework for systematic agg...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Neubert, Peer Schubert, Stefan Tech Univ Chemnitz Chemnitz Germany

ISBN: (纸本)9781665445092

image and video descriptors are an omnipresent tool in computer vision and its application fields like mobile robotics. Many hand-crafted and in particular learned image descriptors are numerical vectors with a potentially (very) large number of dimensions. Practical considerations like memory consumption or time for comparisons call for the creation of compact representations. In this paper, we use hyperdimensional computing (HDC) as an approach to systematically combine information from a set of vectors in a single vector of the same dimensionality. HDC is a known technique to perform symbolic processing with distributed representations in numerical vectors with thousands of dimensions. We present a HDC implementation that is suitable for processing the output of existing and future (deep learning based) image descriptors. We discuss how this can be used as a framework to process descriptors together with additional knowledge by simple and fast vector operations. A concrete outcome is a novel HDC-based approach to aggregate a set of local image descriptors together with their image positions in a single holistic descriptor. The comparison to available holistic descriptors and aggregation methods on a series of standard mobile robotics place recognition experiments shows a 20% improvement in average performance and > 2x better worst-case performance compared to runner-up.

关键词： Computer vision Systematics Aggregates Semantics Memory management Layout Tools

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Design of the Bundle Adjustment FPGA-SoC Architecture for Real Time Vision Based SLAM in AR Glasses 21

Design of the Bundle Adjustment FPGA-SoC Architecture for Re...

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21st International conference on Control, Automation and Systems (ICCAS)

作者： Son, Yeseul Choi, Kwang-Soon Kim, Dohoon Korea Elect Technol Inst VR AR Res Ctr Seoul 03924 South Korea

ISBN: (纸本)9788993215212

As a method to reduce the load on the CPU in AR glasses, where available resources are limited, research is being actively conducted on a method of additionally attaching a dedicated processor for a specific function. In order to display the augmented image on the augmented reality glasses, it is essential to understand the user's location and surrounding environment, which is mainly calculated using the camera image. image processing is the process that places the greatest burden on the CPU due to the large amount of data, and the Bundle Adjustment operation, which adjusts and updates the map, occupies the largest portion. Since it is difficult to perform in real time in an embedded environment, various methods have been tried. In this paper, we propose an FPGA-SoC structure that reduces CPU load by designing bundle adjustment operation as a HW module. The Bundle Adjustment module implemented in HW is implemented as a pipeline and parallel processing operation, and in addition, a memory structure is proposed for efficient data input and output of the module. The design is planned to be made with ASIC, and aims to be used as the co-processor of AR glasses.

关键词： FPGA SLAM Bundle Adjustment (BA) HW accelerator FPGA-SoC AR image processing

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An unsupervised deep learning framework for gene regulatory network inference from single-cell expression data

An unsupervised deep learning framework for gene regulatory ...

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IEEE International conference on Bioinformatics and Biomedicine (BIBM)

作者： Guo Mao Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Laboratory of Software Engineering for Complex System National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

Recent advances in single-cell RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for reconstruction gene regulation networks (GRNs). At present, many different models have been proposed to infer GRN from a large number of RNA-seq data, but most deep learning models use a priori gene regulatory network to infer potential GRNs. It is a challenge to reconstruct GRNs from scRNA-seq data due to the noise and sparsity introduced by the dropout effect. Here, we propose GAALink, a novel unsupervised deep learning method. It first constructs the gene similarity matrix and then refines it by threshold value. It then learns feature representations of genes through a graphical attention autoencoder that propagates information across genes with different weights. Finally, we use gene feature expression for matrix completion such that the GRNs are reconstructed. Compared with seven existing GRNs reconstruction methods, GAALink achieves more accurate performance on seven scRNA-seq dataset with four ground truth networks. GAALink can provide a useful tool for inferring GRNs for scRNA-seq expression data.

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Robust distributed MIMO Localization based on Binary Hard Weighting

Robust Distributed MIMO Localization based on Binary Hard We...

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IEEE International conference on Signal and image processing (ICSIP)

作者： Zhang-Lei Shi Wenxin Xiong Xiao Peng Li Weiguo Li Yaru Fu Xijun Liang College of Science China University of Petroleum (East China) Qingdao China Department of Electrical Engineering City University of Hong Kong Hong Kong China College of Electronics and Information Engineering Shenzhen University Shenzhen China School of Science and Technology Hong Kong Metropolitan University Hong Kong China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

In practical scenarios, the non-line-of-sight errors or impulsive noise in dense urban areas pose great challenges to efficient and robust distributed multiple-input multiple-output radar localization. To achieve robustness against outliers, this paper proposes integrating data selection and localization into a unified framework based on the $\ell_distributed$ -norm optimization and the idea of hard weighting. To be specific, the proposed model introduces a binary auxiliary variable to indicate the outlier-contaminated and unpolluted bistatic range measurements with 0 and 1, respectively. Considering the sparse nature of outliers, we impose sparsity constraint on the binary variable. Then, the robust localization task is cast in the form of mixed integer programming with an $\ell_distributed$ -norm constraint. To address the resultant problem, the alternating minimization algorithm is adopted as the solver, where the target location and the binary variable are updated in an alternating manner. In particular, both subtasks have closed-form solutions with low computational complexity. Through numerical experiments, the efficiency and accuracy of the proposed algorithm is verified in comparison to several competing methods.

关键词： Location awareness Integer programming Closed-form solutions Computational modeling Urban areas Robustness Numerical models

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