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The Design and Implementation of Secure distributed Image Classification Model Training System for Heterogenous Edge computing 16th

The Design and Implementation of Secure Distributed Image Cl...

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16th EAI international conference on Collaborative computing: networking, Applications, and Worksharing, CollaborateCom 2020

作者： Cheng, Cong Dai, Huan Li, Lingzhi Wang, Jin Gu, Fei School of Computer Science and Technology Soochow University Suzhou215006 China School of Electronics and Information Engineering Suzhou University of Science and Technology Suzhou215009 China

ISBN: (纸本)9783030675363

Deep learning provides many new and efficient solutions for edge computing. We study training image classification models on edge devices in this paper. Although there have been many researches on deep learning in edge computing. Most of them did not consider the impact of the limited service capabilities of edge devices, the problem of straggler and insecurity of training data on the system. We design a new distributed computing system to train image classification models on edge devices. To be more specific, we vectorize the convolutional neural network (CNN) to transform it to a lot of matrix multiplications. These matrix multiplications can be arbitrarily cut into many smaller matrix multiplications suitable for computing on edge devices. Besides, our system utilizes codes to ensure the stability and security of distributed matrix multiplications on edge devices. In the performance evaluation, we test the performance of matrix multiplications and a CNN model training in our system with uncoded and coded strategies. The evaluation results show that the system with code strategies perform better than with uncoded strategies on the edge devices having the problem of straggler. In summary, we design a secure distributed image classification model training system for heterogenous edge computing. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Image classification

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Distributional Knowledge Transfer for Heterogeneous Federated Learning

Distributional Knowledge Transfer for Heterogeneous Federate...

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

作者： Huan Wang Lijuan Wang Jun Shen School of Cyber Engineering Xidian University Xi'an China School of Computing & Information Technology University of Wollongong Wollongong Australia

Federated learning (FL) produces an effective global model by aggregating multiple client weights trained on their private data. However, it is common that the data are not independently and identically distributed (non-IID) across different clients, which greatly degrades the performance of the global model. We observe that existing FL approaches mostly ignore the distribution information of client-side private data. Actually, the distribution information is a kind of structured knowledge about the data itself, and it also represents the mutual clustering relations of data examples. In this work, we propose a novel approach, namely Federated Distribution Knowledge Transfer (FedDKT), that alleviates heterogeneous FL by extracting and transferring the distribution knowledge from diverse data. Specifically, the server learns a lightweight generator to generate data and broadcasts it to the sampled clients, FedDKT decouples the feature representations of the generated data and transfers the distribution knowledge to assist model training. In other words, we exploit the similarity and shared parts of the generated data and local private data to improve the generalization ability of the FL global model and promote representation learning. Further, we also propose the similarity measure and attention measure strategies, which implement FedDKT by capturing the correlations and key dependencies among data examples, respectively. The comprehensive experiments demonstrate that FedDKT significantly improves the performance and convergence rate of the FL global model, especially when the data are extremely non-IID. In addition, FedDKT is also effective when the data are identically distributed, which fully illustrates the generalization and effectiveness of the distribution knowledge.

关键词： Training Representation learning Knowledge engineering Correlation Federated learning distributed databases Data models

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Secure Data Contribution and Retrieval in Social Networks Using Effective Privacy Preserving Data Mining Techniques 1

Secure Data Contribution and Retrieval in Social Networks Us...

引用

1st international conference on computing, Communication and Green Engineering, CCGE 2021

作者： Ambani, Dharmendra Atkotiya, Kishor Harivandana College Department of Computer Science Rajkot India Saurashtra University Department of Statistics Rajkot India

ISBN: (纸本)9781665415095

Privacy Preserving Data-Mining is the major use of data- mining methods for ensuring privacy of personal information. Data-mining algorithms search for the information that is most valuable. A major aspect of Privacy Preserving Data Mining is the safeguarding of sensitive information against unauthorized access. The Secure Data Contribution Retrieval algorithm assigns a privacy policy and security is assigned depending on the application needs and compatibility. This method is capable of meeting the requirements for numerous datasets. Currently, social media sites such as Facebook, Twitter, and YouTube are quite popular. Then, the expanded attribute-based encryption methodology allows users to transfer data contents across orbit software networks. Data leakage occurs during the gathering and storage of user Orbit Software Networks in an insecure distributed or centralized system. Third, the suggested Level by Level Security Optimization and Content Visualization algorithm helps prevent privacy problems when sharing information and visualizing data. They use privacy levels at the individual level following the assessment of the privacy compatibility of orbit software networks application. Experimental analysis employs the data from social datasets. © 2021 IEEE.

关键词： Social networking (online)

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Autonomous distributed Systems of Myopic Mobile Robots with Lights 21

Autonomous Distributed Systems of Myopic Mobile Robots with ...

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22nd international conference on distributed computing and networking (ICDCN)

作者： Kamei, Sayaka Hiroshima Univ Grad Sch Adv Sci & Engn Higashihiroshima Hiroshima Japan

ISBN: (纸本)9781450389334

The cooperation of swarming autonomous mobile robots has received significant interests in recent years. The common goal of research on it is to clarify the minimum capabilities for robots to achieve a given task. Thus, algorithms for mobile robots have been considered on a theoretical model with negative assumptions about each robot capabilities[4, 6]. Concerning the assumptions, each robot is identical (i.e., all robots run the same algorithm), anonymous (i.e., each robot has no IDs to distinguish two robots), oblivious (i.e., each robot has no memory to record past situation) and silent (i.e., each robot has no direct means of communication). In the initial model described above, because of fundamentally weak capabilities, it is known that most tasks cannot be solved without some additional assumptions even if the model considers the unlimited visibility. However, we may not need to assume completely oblivious robots considering their implementations on real devices because persistent memory is widely available. Thus, recently, luminous robots have attracted attention in order to improve robot capability [1, 7]. A luminous robot maintains a non-volatile visible light, and emits the chosen light color among a set of colors to other robots. That is, a luminous robot uses the light to memorize its state and communicate with other robots. On the other hand, for the assumption of unlimited visibility of the initial model, each robot has a too strong observation device. Therefore, several recent studies focus on myopic robots to consider the implementation of the robot system [2, 3, 5]. A myopic robot has limited visibility, i.e., it can take a snapshot only within a certain fixed distance. In this talk, we consider myopic robots with lights as a more realistic model and briefly show some recent results on the model.

关键词： swarm robotics LCM robot system myopic luminous

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Visually-defined Real-Time Orchestration of IoT Systems 20

Visually-defined Real-Time Orchestration of IoT Systems

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17th EAI international conference on Mobile and Ubiquitous Systems - computing, networking and Services (MobiQuitous)

作者： Silva, Margarida Dias, Joao Pedro Restivo, Andre Ferreira, Hugo Sereno Univ Porto Fac Engn MIEIC Porto Portugal Univ Porto Fac Engn DEI Porto Portugal Univ Porto Fac Engn INESC TEC Porto Portugal Univ Porto Fac Engn LIACC Porto Portugal

ISBN: (纸本)9781450388405

In this work, we propose a method for extending Node-RED to allow the automatic decomposition and partitioning of the system towards higher decentralization. We provide a custom firmware for constrained devices to expose their resources, as well as new nodes and modifications in the Node-RED engine that allow automatic orchestration of tasks. The firmware is responsible for low-level management of health and capabilities, as well as executing MicroPython scripts on demand. Node-RED then takes advantage of this firmware by (1) providing a device registry allowing devices to announce themselves, (2) generating MicroPython code from dynamic analysis of flow and nodes, and (3) automatically (re-)assigning nodes to devices based on pre-specified properties and priorities. A mechanism to automatically detect abnormal run-time conditions and provide dynamic self-adaptation was also explored. Our solution was tested using synthetic home automation scenarios, where several experiments were conducted with both virtual and physical devices. We then exhaustively measured each scenario to allow further understanding of our proposal and how it impacts the system's resiliency, efficiency, and elasticity.

关键词： Internet-of-Things Orchestration distributed Systems Real-Time Systems Embedded computing

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Accord: Application-driven networking in the datacenter 21

Accord: Application-driven networking in the datacenter

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14th IEEE/ACM international conference on Utility and Cloud computing, UCC 2021

作者： Mortazavi, Seyed Hossein Shafieirad, Hossein Bahnasy, Mahmoud Munir, Ali Cheng, Yuanhui Das, Anudeep Ganjali, Yashar Huawei Technologies Canada University of Waterloo Canada

ISBN: (纸本)9781450385640

Resource optimization algorithms in the cloud are ever more data-driven and decision-making has become reliant on more and more data flowing from different cloud components. Applications and the network control layer on the other hand mainly operate in isolation without direct communication. Recently, increased integration between the network and application has been advocated to benefit both the application and the network but the information exchange has mostly been limited to flow level information. We argue that in the realm of datacenter networks, sharing additional information such as the function processing times and deployment data for planning jobs and tasks can result in major optimization benefits for the network. In this study we present Accord as a Network Application Integration solution to achieve a holistic network-application management solution. We propose a protocol as an API between the network and application then we build a system that uses the processing and networking data from the application to perform network scheduling and routing optimizations. We demonstrate that for a sample distributed learning application, an Accord enhanced solution that uses the application processing information can yield up to 27.8% reduction in Job Completion Time (JCT). In addition, we show how Accord can yield better results for routing decisions through a reinforcement learning algorithm that outperforms first shortest path first by %13. © 2021 ACM.

关键词： Decision making

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Best Community Discovery on Dynamic Social Networks

Best Community Discovery on Dynamic Social Networks

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

作者： Chuanyu Zong Wenlu Wu Tao Qiu Anzhen Zhang Xiufeng Xia School of Computer Science Shenyang Aerospace University Shenyang China

The $k$ -core model is a widely used metric of community cohesion. The problem of best community discovery based on $k$ -core model and $k$ -core function is to find the best $k$ -core communities with the highest scores in a social network. The current best community discovery methods only appropriate for static social networks, however, real-world social networks always evolve over time. Adding or deleting points and edges might not only modify the structure of the networks but also have an impact on the results of best community discovery. To address this issue, therefore, we studied the problem of best community discovery in dynamic graphs. We first define a novel index structure KHS-Index based on $k$ -core hierarchical decomposition and a $k$ -core scoring function. Then, we propose a basic algorithm by reconstructing the index structure on the update graphs. And then we propose a incremental algorithm based on incremental computation strategy for the scores of $k$ -core communities. Furthermore, By investigating the improved core maintenance method and index maintenance method, we explore an effective dynamic update algorithm to find the best communities on dynamic social networks. Finally, comprehensive experiments on real datasets are conducted, which show that our algorithms can effectively and efficiently find best communities on dynamic social networks.

关键词： Measurement Social networking (online) Heuristic algorithms Clustering algorithms Maintenance engineering Indexes

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distributed Many-to-Many Protein Sequence Alignment using Sparse Matrices

Distributed Many-to-Many Protein Sequence Alignment using Sp...

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international conference on High Performance computing, networking, Storage and Analysis (SC)

作者： Selvitopi, Oguz Ekanayake, Saliya Guidi, Giulia Pavlopoulos, Georgios A. Azad, Ariful Buluc, Aydin Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA Microsoft Corp Redmond WA 98052 USA Univ Calif Berkeley Berkeley CA 94720 USA BSRC Alexander Fleming Inst Fundamental Biomed Res 34 Fleming St Vari 16672 Greece Indiana Univ Bloomington IN 47405 USA

ISBN: (纸本)9781728199986

Identifying similar protein sequences is a core step in many computational biology pipelines such as detection of homologous protein sequences, generation of similarity protein graphs for downstream analysis, functional annotation, and gene location. Performance and scalability of protein similarity search have proven to he a bottleneck in many bioinformatics pipelines due to increase in cheap and abundant sequencing data. This work presents a new distributed-memory software PASTIS. PASTIS relies on sparse matrix computations for efficient identification of possibly similar proteins. We use distributed sparse matrices for scalability and show that the sparse matrix infrastructure is a great lit for protein similarity search when coupled with a fully-distributed dictionary of sequences that allow remote sequence requests to be fulfilled. Our algorithm incorporates the unique bias in amino acid sequence substitution in search without altering basic sparse matrix model, and in turn, achieves ideal scaling up to millions of protein sequences.

关键词： Proteins

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Access control method for air ticket distribution system based on blockchain

Access control method for air ticket distribution system bas...

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

作者： Lizhe Zhang Tong Wang Zhijun Wu School of Safety Science and Engineering Civil Aviation University of China Tianjin China School of Electronic Information and Automation Civil Aviation University of China Tianjin China

The air ticket distribution system is a platform to integrate the resources of ticket agents around the ticket sales. It is responsible for connecting ticket suppliers and passengers, and mainly undertakes the services of flight inquiries, air ticket reservation and ticket issuance. However, due to the insufficient access control authority and lax internal supervision and other reasons, passengers' privacy information is frequently leaked, which seriously damage the interests of passengers and airlines. Based on the analysis of ticket distribution process, this paper proposes a blockchain-based access control model for ticket distribution system, aiming at protecting passengers' privacy information and property security. The model is based on Hyperledger Fabric 1.0, which uses the SM2 cryptographic algorithm to replace the default algorithm of the ledger. Experimental results show that the access control method based on blockchain can realize distributed access decision and prevent passenger information from being tampered with illegally. Compared with RSA algorithm, SM2 algorithm is used in the test to generate employee transactions, generate PNR transaction and permission query transaction. The total time is decreased by 17.13%, which is more efficient in each single transaction. Therefore, it is feasible and necessary to the reform of the ledger cryptographic algorithm.

关键词： Access control Privacy Analytical models distributed ledger Atmospheric modeling Process control Control systems

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Scalable distributed High-Order Stencil Computations

Scalable Distributed High-Order Stencil Computations

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Supercomputing conference

作者： Mathias Jacquelin Mauricio Araya–Polo Jie Meng Cerebras Systems Sunnyvale California USA TotalEnergies EP Research & Technology US LLC Houston Texas USA

ISBN: (纸本)9781665454452

Stencil computations lie at the heart of many scientific and industrial applications. Stencil algorithms pose several challenges on machines with cache based memory hierarchy, due to low re-use of memory accesses if special care is not taken to optimize them. This work shows that for stencil computation a novel algorithm that leverages a localized communication strategy effectively exploits the second generation Cerebras Wafer-Scale Engine (WSE-2), which has no cache hierarchy. This study focuses on a 25-point stencil finite-difference method for the 3D wave equation, a kernel frequently used in earth modeling as numerical simulation. In essence, the algorithm trades memory accesses for data communication and takes advantage of the fast communication fabric provided by the architecture. The algorithm -historically memory-bound- becomes compute-bound. This allows the implementation to achieve near perfect weak scaling, reaching up to 503 TFLOPs on WSE-2, a figure that only full clusters can eventually yield.

关键词： Semiconductor device modeling Solid modeling Three-dimensional displays Computational modeling Memory architecture Clustering algorithms Throughput

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