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DynaComm: Accelerating distributed CNN training between edges and clouds through dynamic communication scheduling

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arXiv 2021年

作者： Cai, Shangming Wang, Dongsheng Wang, Haixia Lyu, Yongqiang Xu, Guangquan Zheng, Xi Vasilakos, Athanasios V. Department of Computer Science and Technology Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518066 China Big Data School Qingdao Huanghai University Qingdao266427 China College of Intelligence and Computing Tianjin University Tianjin300350 China Department of Computing Macquarie University SydneyNSW2109 Australia College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China School of Electrical and Data Engineering University of Technology Sydney Australia Department of Computer Science Electrical and Space Engineering Lulea University of Technology Lulea97187 Sweden

To reduce uploading bandwidth and address privacy concerns, deep learning at the network edge has been an emerging topic. Typically, edge devices collaboratively train a shared model using real-time generated data through the Parameter Server framework. Although all the edge devices can share the computing workloads, the distributed training processes over edge networks are still time-consuming due to the parameters and gradients transmission procedures between parameter servers and edge devices. Focusing on accelerating distributed Convolutional Neural Networks (CNNs) training at the network edge, we present DynaComm, a novel scheduler that dynamically decomposes each transmission procedure into several segments to achieve optimal layer-wise communications and computations overlapping during run-time. Through experiments, we verify that DynaComm manages to achieve optimal layer-wise scheduling for all cases compared to competing strategies while the model accuracy remains untouched. Copyright © 2021, The Authors. All rights reserved.

关键词： Edge computing

The Battery Capacity of Energy-storing Quantum Systems

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arXiv 2023年

作者： Yang, Xue Yang, Yan-Han Alimuddin, Mir Salvia, Raffaele Fei, Shao-Ming Zhao, Li-Ming Nimmrichter, Stefan Luo, Ming-Xing The School of Information Science and Technology Southwest Jiaotong University Chengdu610031 China School of Computer and Network Security Chengdu University of Technology Chengdu610059 China Department of Physics of Complex Systems S. N. Bose National Center for Basic Sciences Kolkata700106 India Scuola Normale Superiore PisaI-56127 Italy School of Mathematical Sciences Capital Normal University Beijing100048 China Max-Planck-Institute for Mathematics in the Sciences Leipzig04103 Germany Naturwissenschaftlich-Technische Fakultät Universität Siegen Siegen57068 Germany CAS Center for Excellence in Quantum Information and Quantum Physics Hefei230026 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China

The quantum battery capacity is introduced in this letter as a figure of merit that expresses the potential of a quantum system to store and supply energy. It is defined as the difference between the highest and the lowest energy that can be reached by means of the unitary evolution of the system. This function is closely connected to the ergotropy, but it does not depend on the temporary level of energy of the system. The capacity of a quantum battery can be directly linked with the entropy of the battery state, as well as with measures of coherence and entanglement. © 2023, CC BY.

关键词： Quantum optics

Accelerating NATO Transformation with SnTEE: Experiments with Network security Function Virtualization in Coalition Networks

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Accelerating NATO Transformation with SnTEE: Experiments wit...

International Conference on Military Communications and Information Systems (ICMCIS)

作者： Roberto Rigolin F. Lopes Johannes F. Loevenich Konrad Wrona Paulo H. L. Rettore Jerry Falkcrona Joseph Mathews Nils Nordbotten Bogdan Vasilache Thorsten Lampe Olwen L. Worthington Juha Röning Secure Communications & Information (SIX) Thales Deutschland Ditzingen Germany Department of Mathematics/Computer Science University of Osnabrück Osnabrück Germany NATO Cyber Security Centre The Hague Netherlands Military University of Technology Warsaw Poland Communication Systems (KOM) Fraunhofer FKIE Bonn Germany Cyber Defence and C2 Technology Swedish Defence Research Agency Linköping Sweden Information Technology Division U.S. Naval Research Laboratory Washington DC USA Thales Norway Oslo Norway WonderBit B.V. Almere The Netherlands Defence Science and Technology Laboratory (Dstl) UK Ministry of Defence London United Kingdom Information Technology and Electrical Engineering University of Oulu Oulu Finland

This paper introduces a virtual laboratory for experiments with cybersecurity functions in coalition networks. The motivation comes from the NATO Allied Command Transformation (ACT) initiative on Science and Technology Experimentation Environment (SnTEE), which supports the transfer of scientific and technical activities carried out by the NATO Science and Technology Organization (STO) research task groups towards NATO operations. The goal of the experiments reported in this paper is to emulate a network topology inspired by Protected Core Networking (PCN), which defines two interfaces. One interface between two protected core segments, from different nations, and another interface between a protected core segment and a colored cloud within a particular nation. The experiments are intended to support the investigation performed by the STO IST-196 RTG on Cyber security in Virtualized Networks. In particular, the starting hypothesis of IST-196 is that security function virtualization can support/speedup PCN topology changes when links are added/removed. We define our experimental setup as Infrastructure as Code (IaC) and introduce experiments that emulate topology changes. We also sketch a solution for the deployment of two chains of virtualized cybersecurity functions at lower protocol layers (physical and IP) and at higher protocol layers (transport and application). This paper also lists future experiments for the NATO SnTEE lab, namely risk-aware routing, cross-layer enforcement of policies, information exchange functions, and federate monitoring and detection of cyber incidents/attacks.

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反向连边在大型分层网络中的影响（英文）

engineering 2024年第5期 240-249页

作者：曹浩森胡斌斌莫小雨陈都鑫高建喜袁烨陈关荣 Tamás Vicsek 管晓宏张海涛 MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Mechanical and Aerospace Engineering Nanyang Technological University Jiangsu Key Laboratory of Networked Collective Intelligence School of Mathematics Southeast University Department of Computer Science Rensselaer Polytechnic Institute Department of Electronic Engineering City University of Hong Kong Department of Biological Physics E?tv?s University MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and Engineering Xi'an Jiaotong University

Hierarchical networks are frequently encountered in animal groups, gene networks, and artificial engineering systems such as multiple robots, unmanned vehicle systems, smart grids, wind farm networks,and so forth. The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes, such as lagging birds' howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social group. This study reveals that, for most large-scale real hierarchical networks, the majority of the reverse edges do not affect the synchronization process of the entire network; the synchronization process is influenced only by a small part of these reverse edges along specific paths. More surprisingly, a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%. The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork. The overwhelming majority of active reverse edges turn out to have some kind of ‘‘bunchingo effect on the information flows of hierarchical networks, which slows down synchronization processes. This finding refines the current understanding of the role of reverse edges in many natural, social, and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these networks. Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

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An Enhanced Feature Selection Approach based on Mutual Information for Breast Cancer Diagnosis 6

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An Enhanced Feature Selection Approach based on Mutual Infor...

6th International Conference on Image and Signal Processing and their Applications, ISPA 2019

作者： Zemmal, Nawel Azizi, Nabiha Ziani, Amel Benzebouchi, Nacer Eddine Aldwairi, Monther Badji Mokhtar University Labged Laboratory Annaba23000 Algeria Department of Mathematics and Computer Science Mohamed Cherif Messaadia University Souk-Ahras41000 Algeria Lri Laboratory: Information Research Laboratory Computer Science Department Annaba23000 Algeria Jordan University of Science and Technology Department of Network Engineering and Security Irbid22110 Jordan

ISBN: (纸本)9781728131566

Breast cancer is the most feared disease in the female population. Early detection plays an important role to improve prognosis. Mammography is the best examination for the detection of breast cancer. However, in some cases, reading mammograms is difficult for radiologists. For this reason, several researches have been conducted to develop computer Aided Diagnosis tools (CAD) for this disease which aims to interpret mammography images. This paper investigates a new CAD system based on Transductive scheme and Mutual Information for breast abnormalities diagnosis. In the proposed method, a feature vector contains a combination of two features extraction method: Grey Level Co-occurrence Matrix and local Binary Pattern. In the next step, a novel scheme combining Mutual Information and Correlation-based feature selection was applied for selecting the most relevant features. Finally, the classification was achieved using a Transductive Support Vector Machine classifier. The effectiveness of the proposed CAD is examined on the DDSM dataset using classification accuracy, recall and precision. Experimental results demonstrate that the proposed CAD system is clinically significant and can be used to classify the abnormalities of the breast. © 2019 IEEE.

关键词： computer aided diagnosis

LR-RRA-CCA secure functional encryption for randomized functionalities from trapdoor HPS and LAF

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Science China(Information Sciences) 2018年第5期61卷 291-305页

作者： Huige WANG Kefei CHEN Baodong QIN Ziyuan HU Department of Network Engineering Anhui Science and Technology University Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Mathematics Hangzhou Normal University Westone Cryptologic Research Center National Engineering Laboratory for Wireless Security Xi'an University of Posts and Telecommunications State Key Laboratory of Cryptology

Original public-key encryption is viewed as a tool to encrypt point-to-point *** data is aimed at a particular user,where a sender encrypts a message x under a specified public key P *** this case,only the owner of the(unique)secret key corresponding to P K can decrypt the resulting ciphertext to recover the message ***,on some occasions,encryption is necessary to implement more fine-grained control of the encrypted *** example,the sender

关键词： LAF CCA RRA

VFL: A Verifiable Federated Learning with Privacy-Preserving for Big Data in Industrial IoT

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arXiv 2020年

作者： Fu, Anmin Zhang, Xianglong Xiong, Naixue Gao, Yansong Wang, Huaqun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Northeastern State University Department of Mathematics and Computer Science TahlequahOK United States Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

Due to the strong analytical ability of big data, deep learning has been widely applied to model the collected data in industrial IoT. However, for privacy issues, traditional data-gathering centralized learning is not applicable to industrial scenarios sensitive to training sets. Recently, federated learning has received widespread attention, since it trains a model by only relying on gradient aggregation without accessing training sets. But existing researches reveal that the shared gradient still retains the sensitive information of the training set. Even worse, a malicious aggregation server may return forged aggregated gradients. In this paper, we propose the VFL, verifiable federated learning with privacy-preserving for big data in industrial IoT. Specifically, we use Lagrange interpolation to elaborately set interpolation points for verifying the correctness of the aggregated gradients. Compared with existing schemes, the verification overhead of VFL remains constant regardless of the number of participants. Moreover, we employ the blinding technology to protect the privacy of the gradients submitted by the participants. If no more than n-2 of n participants collude with the aggregation server, VFL could guarantee the encrypted gradients of other participants not being inverted. Experimental evaluations corroborate the practical performance of the presented VFL framework with high accuracy and efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Analysis of variance of graph-clique mining for scalable proof of work 14th

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Analysis of variance of graph-clique mining for scalable pro...

14th International Conference on Information security and Cryptology, Inscrypt 2018

作者： Anada, Hiroaki Matsushima, Tomohiro Su, Chunhua Meng, Weizhi Kawamoto, Junpei Bag, Samiran Sakurai, Kouichi Department of Information Security University of Nagasaki Nagasaki Japan Department of Informatics Graduate School and Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Division of Computer Science University of Aizu Aizuwakamatsu Japan Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark School of Computing Science Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9783030142339

Recently, Bitcoin is becoming one of the most popular decentralized cryptographic currency technologies, and Bitcoin mining is a process of adding transaction records to Bitcoin’s public ledger of past transactions or blockchain. To obtain a bitcoin, the mining process involves compiling recent transactions into blocks and trying to solve a computationally difficult puzzle, e.g., proof of work puzzle. A proof of work allows miners the ability to quantify how much work a given proof contains. Basically, the required time for mining is decided in advance, but problems will occur if the value is large for dispersion. In this paper, we first accept that the required time between consecutive blocks follows the exponential distribution. That is, the variance is stable as long as the expected time is fixed. Then, we focus on the graph clique mining technique proposed by the literature, like Tromp (BITCOIN 2015) and Bag-Ruj-Sakurai (Inscrypt 2015), which is based on a computational difficulty problem of searching cliques of undirected graphs, where a clique is a subset of vertices. In particular, when the clique size is two, graph clique mining can be used to gain Bitcoins. The previous work also claimed that if the clique size is parameterized and increased, even if the expected time is fixed, the variance would not be stable. However, no qualitative or quantitative results were given to support their claim. Motivated by this issue, in this work, we propose a simple search algorithm for graph cliques mining, and perform a small scale evaluation on Bitcoin and Graph cliques’s solo mining to investigate the variance issue. © 2019, Springer Nature Switzerland AG.

关键词： Blockchain

VerifyTL: Secure and verifiable collaborative transfer learning

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arXiv 2020年

作者： Ma, Zhuoran Ma, Jianfeng Miao, Yinbin Liu, Ximeng Zheng, Wei Choo, Kim-Kwang Raymond Deng, Robert H. School of Cyber Engineering Xidian University Xi'an710071 China Shaanxi Key Laboratory of Network and System Security Xidian University Xi'an710071 China Key Laboratory of Information Security of Network Systems College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX78249 United States School of Information Systems Singapore Management University Singapore188065 Singapore

Getting access to labelled datasets in certain sensitive application domains can be challenging. Hence, one often resorts to transfer learning to transfer knowledge learned from a source domain with sufficient labelled data to a target domain with limited labelled data. However, most existing transfer learning techniques only focus on one-way transfer which brings no benefit to the source domain. In addition, there is the risk of a covert adversary corrupting a number of domains, which can consequently result in inaccurate prediction or privacy leakage. In this paper we construct a secure and Verifiable collaborative Transfer Learning scheme, VerifyTL, to support two-way transfer learning over potentially untrusted datasets by improving knowledge transfer from a target domain to a source domain. Further, we equip VerifyTL with a cross transfer unit and a weave transfer unit employing SPDZ computation to provide privacy guarantee and verification in the two-domain setting and the multi-domain setting, respectively. Thus, VerifyTL is secure against covert adversary that can compromise up to n − 1 out of n data domains. We analyze the security of VerifyTL and evaluate its performance over two real-world datasets. Experimental results show that VerifyTL achieves significant performance gains over existing secure learning schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge management