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2023 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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Scavenger: Better Space-Time Trade-Offs for Key-Value Separated LSM-trees 40

Scavenger: Better Space-Time Trade-Offs for Key-Value Separa...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhang, Jianshun Wang, Fang Qiu, Sheng Wang, Yi Ou, Jiaxin Huang, Junxun Li, Baoquan Fang, Peng Feng, Dan School of Computer Science and Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China China Shenzhen Huazhong University of Science and Technology Research Institute China Bytefrance Inc.

ISBN: (纸本)9798350317152

Key-Value Stores (KVS) implemented with log-structured merge-tree (LSM-tree) have gained widespread ac-ceptance in storage systems. Nonetheless, a significant challenge arises in the form of high write amplification due to the compaction process. While KV-separated LSM-trees successfully tackle this issue, they also bring about substantial space am-plification problems, a concern that cannot be overlooked in cost-sensitive scenarios. Garbage collection (GC) holds significant promise for space amplification reduction, yet existing GC strategies often fall short in optimization performance, lacking thorough consideration of workload characteristics. Additionally, current KV-separated LSM-trees also ignore the adverse effect of the space amplification in the index LSM-tree. In this paper, we systematically analyze the sources of space amplification of KV-separated LSM-trees and introduce Scavenger, which achieves a better trade-off between performance and space amplification. Scavenger initially proposes an I/O-efficient garbage collection scheme to reduce I/O overhead and incorporates a space-aware compaction strategy based on compensated size to minimize the space amplification of index LSM-trees. Extensive experiments show that Scavenger significantly improves write performance and achieves lower space amplification than other KV-separated LSM-trees (including BlobDB, Titan, and TerarkDB). © 2024 IEEE.

关键词： Economic and social effects

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Advanced Data Segmentation and Encryption Approach using Fog Computing

Advanced Data Segmentation and Encryption Approach using Fog...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Singh, Kamred Udham Varshney, Neeraj Kumar, Pushpendra Sharma, Pradeep Kumar Mishra, Umang Deep Singh, Teekam Graphic Era Hill University School of Computing Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Scanno System Greater Noida India Hcl Technology UP India Unique Institute of Management & Technology Muradnagar India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350382723

Data security in conventional applications was only available at the application layer. Within the scope of the intended research, the safety of the merchandise is ensured. The deficiencies that are present in the security systems that are already in place have led to the realisation that a new security system is required. Considering that authentication is not present, the likelihood of decryption should be reduced. Establishing a decentralised security system is essential in order to protect against threats coming from a variety of digital networks. The new encryption standard is said to improve multilayer security, according to the claims. The material is split into two halves using this method, which also provides a reliable transmission mechanism. Because of the technology, hackers are unable to create vulnerabilities in the security system. There was a need for security in both the cloud and the edge environment in order to prevent intruders. Increasing the reliability and security of data transmission over a variety of networks was the motivation for the development of the two fish approach. Because of its security mechanism, it has been able to survive assaults from crackers and hackers. The paper that is being presented advises doing research on the safety of edge transmissions that are based on slicing. In order to prevent attackers from launching attacks from several networks, it is necessary to establish decentralised security. Layered security will help increase encryption standards, which is a significant benefit. Over the course of various investigations, computer security edge generators have been the focus of attention. Quite a few of them have already been extensively discussed. For the purpose of developing a reliable method of transmission, the proposed research splits the data into two parts, each of which is further encrypted at a different level. As a result of the development of multilayer encryption, the reliability and safety of data transm

关键词： Fish

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EdgeMove: Pipelining Device-Edge Model Training for Mobile Intelligence 23

EdgeMove: Pipelining Device-Edge Model Training for Mobile I...

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2023 World Wide Web Conference, WWW 2023

作者： Dong, Zeqian He, Qiang Chen, Feifei Jin, Hai Gu, Tao 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 School of Computing Macquarie 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

Training machine learning (ML) models on mobile and Web-of-Things (WoT) has been widely acknowledged and employed as a promising solution to privacy-preserving ML. However, these end-devices often suffer from constrained resources and fail to accommodate increasingly large ML models that crave great computation power. Offloading ML models partially to the cloud for training strikes a trade-off between privacy preservation and resource requirements. However, device-cloud training creates communication overheads that delay model training tremendously. This paper presents EdgeMove, the first device-edge training scheme that enables fast pipelined model training across edge devices and edge servers. It employs probing-based mechanisms to tackle the new challenges raised by device-edge training. Before training begins, it probes nearby edge servers' training performance and bootstraps model training by constructing a training pipeline with an approximate model partitioning. During the training process, EdgeMove accommodates user mobility and system dynamics by probing nearby edge servers' training performance adaptively and adapting the training pipeline proactively. Extensive experiments are conducted with two popular DNN models trained on four datasets for three ML tasks. The results demonstrate that EdgeMove achieves a 1.3 × -2.1 × speedup over the state-of-the-art scheme. © 2023 ACM.

关键词： Internet of things

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Fast Parallel Recovery for Transactional Stream Processing on Multicores

Fast Parallel Recovery for Transactional Stream Processing o...

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International Conference on Data engineering

作者： Jianjun Zhao Haikun Liu Shuhao Zhang Zhuohui Duan Xiaofei Liao Hai Jin Yu Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Transactional stream processing engines (TSPEs) have gained increasing attention due to their capability of processing real-time stream applications with transactional semantics. However, TSPEs remain susceptible to system failures and power outages. Existing TSPEs mainly focus on performance improvement, but still face a significant challenge to guarantee fault tolerance while offering high-performance services. We revisit commonly-used fault tolerance approaches in stream processing and database systems, and find that these approaches do not work well on TSPEs due to complex data dependencies. In this paper, we propose a novel TSPE called MorphStreamR to achieve fast failure recovery while guaranteeing low performance overhead at runtime. The key idea of MorphStreamR is to record intermediate results of resolved dependencies at runtime, and thus eliminate data dependencies to improve task parallelism during failure recovery. MorphStreamR further mitigates the runtime overhead by selectively tracking data dependencies and incorporating workload-aware log commitment. Experimental results show that MorphStreamR can significantly reduce the recovery time by up to 3.1 x while experiencing much less performance slowdown at runtime, compared with other applicable fault tolerance approaches.

关键词： Fault tolerance Runtime Fault tolerant systems Semantics Parallel processing Real-time systems Power system reliability

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Deep Learning Techniques for Advanced Large-Scale Data Processing 15

Deep Learning Techniques for Advanced Large-Scale Data Proce...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sunil, M.P. Shekhawat, Jyoti Vanaja, T. Mohammed, Vazeer Ali Thaware, Shailesh J. Singh, Vaishali Karnataka Bangalore India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Department of Computer & Information System Lewis University Illinois United States Department of Computer Engineering Vishwakarma Institute of Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350370249

Deep getting to know techniques have become increasingly more popular due to their potential to improve huge-scale records processing performance. Leveraging specialized hardware which includes GPUs, deep getting to know algorithms can attain mind-blowing pace will increase compared to traditional algorithms. further, deep gaining knowledge of fashions can discover complicated non-linear relationships in big datasets, permitting greater accurate overall performance and less fake predictions. Latest advances in deep mastering have enabled it to be implemented to remedy a plethora of information processing duties, from photo recognition to natural language processing. CNNs, RNNs, and GANs are a number of the maximum famous deep gaining knowledge of fashions used in tackling complex data processing obligations. agencies consisting of Google, fb, and Amazon use these sophisticated frameworks to build predictive models that help improve numerous programs. Deep getting to know strategies offer exquisite ability to take massive-scale information processing to the following stage. corporations are increasingly more becoming privy to the emerging opportunities, and actively investing in deep mastering to liberate the untapped strength of their information. ©2024 IEEE.

关键词： Large datasets

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SPViT: Accelerate Vision Transformer Inference on Mobile Devices via Adaptive Splitting and Offloading

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IEEE Transactions on Mobile Computing 2025年

作者： Zhao, Sifan Liu, Tongtong Jin, Hai Yao, Dezhong Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

The Vision Transformer (ViT), which benefits from utilizing self-attention mechanisms, has demonstrated superior accuracy compared to CNNs. However, due to the expensive computational costs, deploying and inferring ViTs on resource-constrained mobile devices has become a challenge. To resolve this challenge, we conducted an empirical analysis to identify performance bottlenecks in deploying ViTs on mobile devices and explored viable solutions. In this paper, we propose SPViT, an adaptive split and offloading method that accelerates ViT inference on mobile devices. SPViT executes collaborative inference of ViT across available edge devices. We introduce a fine-grained splitting technique for the vision transformer structure. Furthermore, we propose an algorithm based on the Auto Regression model to predict partition latency and adaptive offload partitions. Finally, we design offline and online optimization methods to minimize the computational and communication overhead on each device. Based on real-world prototype experiments, SPViT effectively reduces inference latency by 2.2x to 3.3x across four state-of-the-art models. © 2002-2012 IEEE.

关键词： Regression analysis

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Identification of a Distant Speaker and Its Robustness*

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Chinese Journal of Electronics 2023年第2期20卷 278-282页

作者： Ye Jiang Zhenmin Tang Longbiao Wang School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Department of System Engineering Shizuoka University Japan

Robust speaker identification is presented for speech recorded by distant microphones. Three compensation approaches are investigated to improve the robustness of speaker identification in such environments. The first approach applies spectral subtraction before feature extraction to reduce the late-reverberation effect. The second approach makes use of feature warping as feature compensation in distant speaker identification under mismatched training-testing conditions. The third approach employs a novel method of initializing Gaussian mixture model parameters: combined division and k-means clustering. The experiment results show that, relative to the baseline system based on CMN, the channel-average recognition rates for the compensated system were 11.4%, 15.4%, 17.0%, and 17.8% higher for the TIMIT database and 6.8%, 6.4%, 9.3%, and 14.0% higher for the JNAS database for four different environments. In addition, the results show that the combination of the three approaches has better performance than the use of a single compensation method.

关键词： Databases Feature extraction Robustness Gaussian mixture model Microphones

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Visualization Analysis and Trends in Indonesian Internet of Things Research Using Bibliometrics 1

Visualization Analysis and Trends in Indonesian Internet of ...

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1st International Conference on Information system and Information Technology, ICISIT 2022

作者： Maulana, Fairuz Iqbal Pangestu, Gusti Febriantono, M. Aldiki Arifuddin, Rahman Widartha, Vandha Pradwiyasma Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia

ISBN: (数字)9781665402002

ISBN: (纸本)9781665402002

The Internet of Things (IoT) has emerged as a critical issue and area of research for a variety of communities, academics, and industries worldwide. Due to the lack of research in IoT and understanding the direction of IoT development in the future. This research presents bibliometric analysis and analysis of science mapping on IoT. Data was taken from the Scopus database based on Indonesian state affiliation from 2012-2020. A total of 1415 documents (Conference proceedings, journals, book series, and books) were retrieved, which were then processed online via Scopus Web and by VOSviewer software to perform advanced bibliometric analysis and science mapping analysis. The method is divided into five stages: keyword selection, initial search results, search result refinement, initial compilation, and data analysis. Scopus's list of the most frequently published and indexed articles, the most published papers are in the subject area of computer science (29.0%) with 938 documents, engineering (20.7%) with 668 documents, Physics and Astronomy (9.5%) with 308 documents. The data processed and visualized on this topic offer exploratory information about the current status and trends in the IoT scientific literature and provide insight for established and novice researchers in understanding this research topic. © 2022 IEEE.

关键词： Mapping

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FFCI: A Camera and IMU Sensors Based Multi-modal Neural Network for Activity Recognition in Smart Factory

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IEEE Transactions on Consumer Electronics 2025年

作者： Wang, Yujue Niu, Xin Lv, Xianwei Yu, Chen Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

Worker activity recognition is an important aspect of the construction of smart factory. The development of deep neural networks and the widespread distribution of sensors in the smart factory have brought opportunities for the recognition of workers' activities. The existing methods based on camera and IMU sensors respectively have problems of visual occlusion and difficulty in recognition of similar activities, which result in the reduction of recognition accuracy. Therefore, we propose a feature-level fusion based on camera and IMU sensor (FFCI) for activity recognition in smart factory. The FFCI employs an optimized multi-modal fusion strategy to integrate the visual information and the action information of workers' activities. In order to verify the effectiveness of FFCI, we define a simple yet fine-grained assembly task and collect six common operations. Furthermore, we add visual occlusion and similar activity data to get closer to the smart factory. Finally, we evaluate the FFCI on the collected dataset and achieve a recognition accuracy of 98.17%, demonstrating its effectiveness in accurately classifying workers' activities. © 1975-2011 IEEE.

关键词： Factory automation

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