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A distributed Privacy-Preserving Framework for Deep Learning with Edge-Cloud computing 15

A Distributed Privacy-Preserving Framework for Deep Learning...

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2022 IEEE Congress on Cybermatics: 15th IEEE international conferences on Internet of things, ithings 2022, 18th IEEE international conferences on Green computing and Communications, GreenCom 2022, 2022 IEEE international conference on Cyber, Physical and Social computing, CPSCom 2022 and 8th IEEE international conference on Smart Data, SmartData 2022

作者： Dai, Fei Liu, Guozhi Huang, Bi Xu, Xiaolong Chen, Chaochao Zhou, Zhangbing Zhou, Xiaokang Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China Nanjing University of Information Science and Technology School of Computer and Software Nanjing China Zhejiang University College of Computer Science and Technology Zhejiang China China University of Geosciences School of Information Engineering Beijing China Shiga University Faculty of Data Science Shiga Japan

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Industrial Internet of things (IIoT) systems can leverage deep learning (DL) models to provide intelligent applications. However, the training process of such DL models tends to leak privacy when the training data contain sensitive operational and management information. Most studies about privacy-preserving DL require a trusted data collector and thus are not suitable in an untrusted environment. In this paper, we propose a distributed privacy-preserving framework for DL with edge-cloud computing, where direct privacy leakage and indirect privacy leakage of training data are both considered. First, a pre-trained convolutional neural network (CNN) is partitioned into two parts for limiting direct privacy leakage of raw data, namely the feature module and the classification module. the feature module consisting of the lower layers of the CNN is deployed on an edge server, which is used to attract the feature of raw data. the feature data is fed to the classification module consisting of the higher layers of the CNN, which is deployed on the cloud server to compute image classification tasks. Second, a perturbation module between the feature module and the classification module is designed for limiting indirect privacy leakage during feature data transmission. the perturbation module uses local differential privacy (LDP) to produce noise in the feature data. third, to further improve the accuracy, we retrain the classification module with the randomized feature data from edge servers. Experimental results show that the proposed framework achieves high accuracy under low privacy budgets. © 2022 IEEE.

关键词： Classification (of information)

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WideVision: A Low-Power, Multi-Protocol Wireless Vision Platform for distributed Surveillance 18

WideVision: A Low-Power, Multi-Protocol Wireless Vision Plat...

引用

18th international conference on Wireless and Mobile computing, Networking and Communications (WiMob)

作者： Scherer, Moritz Sidler, Fabian Rogenmoser, Michael Magno, Michele Benini, Luca Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Zurich Switzerland Univ Bologna Dept Elect Elect & Informat Engn Bologna Italy

ISBN: (数字)9781665469753

ISBN: (纸本)9781665469753

the trend in Internet of things research points toward performing increasingly compute-intensive data analysis tasks on embedded sensor nodes, rather than server centers. Exploiting the technological advances in both energy efficiency, and Tiny Machine Learning algorithms and methods, an increasing number of recognition and classification tasks can be performed by small, low-power, wireless sensor nodes. this paper presents WideVision, a wireless, wide-area sensing platform capable of performing on-board person detection with power requirements in the mW range. the WideVision platform integrates seamlessly into the Internet of things, by coupling a dedicated multiradio platform, including a LoRa interface, enabling medium and long-range communication, with a novel parallel RISC-V microcontroller. We evaluate the proposed platform with the GAP8 microcontroller, which includes an 8-core RISC-V cluster, and greyscale camera to perform person detection by training and deploying an advanced, quantized neural network, achieving a statistical accuracy 84.5% for a 5-person detection task with a latency of only 182 ms. Experimental results demonstrate that the WideVision sensor node platform while performing inference at a rate of one image per minute on-board, is capable of lasting 300 days on a 2400 mAh Li-ion battery, and 65 days when evaluating one image per 10 seconds while providing effective surveillance of its perimeter.

关键词： Wireless communication Training Wireless sensor networks Power measurement Microcontrollers Surveillance Battery charge measurement

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distributed Scheduling with Effective Holdoff Algorithm in Wireless Mesh Networks 18th

Distributed Scheduling with Effective Holdoff Algorithm in W...

引用

Joint conferences on 18th international conference on intelligent Systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired computing , NaBIC 2018

作者： Mathad, K.S. Mangalwede, S.R. KLS Gogte Institute of Technology BelagaviKarnataka India

ISBN: (纸本)9783030166595

Wireless IEEE 802.16 provides high speed internet to mobile users. Resource allocation is required that is robust and efficient which provides mobile users with high throughput. distributed scheduling directs the packets using election algorithm. this algorithm establishes the transmission slots between the competing nodes and uses holdoff algorithm to compute node claimed slots. Mesh node does data transmission without conflicts in multihop networks using control messages. distributed scheduling with effective holdoff method is proposed to improve throughput. Usual holdoff algorithm has low performance whereas in the proposed algorithm it considers number of adjoining nodes and state of a node for scheduling to improve the throughput. © 2020, Springer Nature Switzerland AG.

关键词： Scheduling

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intelligent rush repair of unmanned distribution network based on deep reinforcement learning 18

Intelligent rush repair of unmanned distribution network bas...

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18th IEEE international conference on Mobility, Sensing and Networking (MSN)

作者： Zhao, Yue Chuan, Yang Pu, Shi Han, Xuwen Xia, Shiyu Xie, Yanqi Yangzhou Power Supply Branch State Grid Jiangsu E Yangzhou Jiangsu Peoples R China Xiamen Univ Technol Xiamen Peoples R China

ISBN: (纸本)9781665464574

With the continuous expansion of China's power grid scale and the increasing number of power users year by year, it is necessary to ensure the normal power supply of users. When a power failure occurs, it is particularly critical whether the emergency repair task can be completed quickly and scientifically. In this paper, an intelligent repair model of "unmanned" distribution network based on deep reinforcement learning is proposed, which adopts speech recognition technology and deep reinforcement learning algorithm to achieve the "unmanned" of the whole system. Users can transmit the emergency repair information to the voice recognition module of the power supply emergency repair center by voice, SMS and IMS, and the module will get the emergency repair position and the amount of emergency repair tasks. then, the resource allocation module is used to learn the emergency repair resource allocation strategy online, and the intelligent control of emergency repair in distribution network is realized. To verify the proposed algorithm, it is compared with two typical allocation strategies under the same settings. the results of the experiments demonstrate that the method based on deep reinforcement learning performs better in terms of emergency repair delay and intelligent emergency repair of the power supply in distribution networks.

关键词： failure Speech recognition Emergency maintenance Neural network Deep reinforcement learning

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Construct Digital Twin Models in Cyber Space for Physical Objects of intelligent Optical Network 15

Construct Digital Twin Models in Cyber Space for Physical Ob...

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作者： Kuang, Liwei Wu, Jun Yin, Shan FiberHome Company Network Business Unit Wuhan China FiberHome Company Technology Management Wuhan China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

the optical network has been evolving into intelligent infrastructure in which the physical network can be automatically and predictably managed by the deployed software functions in the cyber space. this paper proposes novel construction approaches of digital twin models for intelligent optical network. A tensor based three layers, three slices, and four features framework was presented to promote the traditional optical network to intelligent information infrastructure. In order to proof and realize the framework, this paper firstly introduces four units, namely device unit, management unit, intelligent unit, and service unit, then illustrates how to construct a cyber physical social system to implement the corresponding functions of these four units. In the cyber space, three types of technologies including digital twin, artificial intelligent, special algorithm are used to analyze date and generate controlling instructions for the optical network devices in the physical space. A cyber physical system prototype for the intelligent optical network was developed and demonstrated. We present two application scenarios of the mentioned prototype, namely quality evaluation of the optical network and prediction of transmission performance. Finally, the paper describes future work and evolution trend of the cyber physical system for intelligent optical network. © 2022 IEEE.

关键词： Cyber Physical System

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Incremental Unsupervised Adversarial Domain Adaptation for Federated Learning in IoT Networks 18

Incremental Unsupervised Adversarial Domain Adaptation for F...

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18th IEEE international conference on Mobility, Sensing and Networking (MSN)

作者： Huang, Yan Du, Mengxuan Zheng, Haifeng Feng, Xinxin Fuzhou Univ Coll Phys & Informat Engn Fujian Key Lab Intelligent Proc & Wireless Transm Fuzhou 350108 Peoples R China

ISBN: (纸本)9781665464574

Federated learning, as an effective machine learning paradigm, can collaboratively training an efficient global model by exchanging the network parameters between edge nodes and the cloud server without sacrificing data privacy. Unfortunately, the obtained global model cannot generalize to newly collected unlabeled data since the unlabeled data collected by different edge devices are diverse. Furthermore, the distributions of collected labeled data and unlabeled data are also different for edge devices. In this paper, we propose a method named Incremental Unsupervised Adversarial Domain Adaptation (IUADA) for federated learning, which aims to reduce the domain shift between the labeled data and unlabeled data in the edge nodes and enhance the performance of the personalized target domain models based on the local unlabeled data. Finally, we evaluate the performance of the proposed method by using three realworld datasets. Extensive experimental results demonstrate that the proposed method is efficient to solve the problem of domain shift and achieves a better performance for unlabeled data for federated learning.

关键词： Federated learning edge computing incremental unsupervised adversarial domain adaptation Internet of things

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Unified signal and data analysis for integration of condition monitoring and intelligent control 18

Unified signal and data analysis for integration of conditio...

引用

18th international conference on Condition Monitoring and Asset Management, CM 2022

作者： Juuso, Esko K. Control Engineering Environmental and Chemical Engineering Faculty of Technology University of Oulu P.O.Box 4300 FI-90014 Finland

ISBN: (纸本)9781713862277

Automatic fault detection with a variety of computational indicators makes it possible to combine reliable condition monitoring with multi-level process control. Various statistical features are widely used in condition monitoring. Current measurements can provide much more and higher order derivations introduce additional possibilities. Furthermore, combinations of real order derivatives and generalized real order norms expand solutions for detecting different signal phenomena. this generalized feature extraction can be tuned to various applications by selecting the sample interval and the orders of derivation and norms. the extensive datasets need edge computing for generating computationally various specific features from the measured signals. the general spectral norms extend this for combining time and frequency domains. the dimensions of all these features are the same as the original signal and all of them can be recursively updated, if necessary, by including new sample intervals of the same size. Nonlinear scaling allows different features to be obtained on the same dimensionless scale, which can also be understood on the basis of natural language. the whole analysis can be done by using waveform signals. Nonlinear scaling expands dimensionless indices so that scaling takes care of nonlinearity processing. In this case, linear interactions are sufficient for the dynamic models used in prognostics and fatigue analysis. © 2022 18th international conference on Condition Monitoring and Asset Management, CM 2022. All rights reserved.

关键词： Condition monitoring

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EdgeLD: Locally distributed Deep Learning Inference on Edge Device Clusters 22

EdgeLD: Locally Distributed Deep Learning Inference on Edge ...

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22nd IEEE international conference on High Performance computing and Communications, 18th IEEE international conference on Smart City and 6th IEEE international conference on Data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Xue, Feng Fang, Weiwei Xu, Wenyuan Wang, Qi Ma, Xiaodong Ding, Yi Beijing Jiaotong University School of Computer and Information Technology Beijing China Key Laboratory of Industrial Internet of Things Networked Control Ministry of Education Chongqing China Beijing Wuzi University School of Information Beijing China

ISBN: (纸本)9781728176499

Deep Neural Networks (DNN) have been widely used in a large number of application scenarios. However, DNN models are generally both computation-intensive and memory-intensive, thus difficult to be deployed on resource-constrained edge devices. Most previous studies focus on local model compression or remote cloud offloading, but overlook the potential benefits brought by distributed DNN execution on multiple edge devices. In this paper, we propose EdgeLD, a new framework for locally distributed execution of DNN-based inference tasks on a cluster of edge devices. In EdgeLD, DNN models' time cost will be firstly profiled in terms of computing capability and network bandwidth. Guided by profiling, an efficient model partition scheme is designed in EdgeLD to balance the assigned workload and the inference runtime among different edge devices. We also propose to employ layer fusion to reduce communication overheads on exchanging intermediate data among devices. Experiment results show that our proposed partition scheme saves up to 15.82% of inference time with regard to the conventional solution. Besides, applying layer fusion can speedup the DNN inference by 1.11-1.13X. When combined, EdgeLD can accelerate the original inference time by 1.77-3.57X on a cluster of 2-4 edge devices. © 2020 IEEE.

关键词： Deep neural networks

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Federated Learning in Robotic and Autonomous Systems 18

Federated Learning in Robotic and Autonomous Systems

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18th international conference on Mobile Systems and Pervasive computing, MobiSPC 2021, the 16th international conference on Future Networks and Communications, FNC 2021 and the 11th international conference on Sustainable Energy Information technology, SEIT 2021

作者： Xianjia, Yu Queralta, Jorge Peña Heikkonen, Jukka Westerlund, Tomi Turku Intelligent Embedded and Robotic Systems Lab University of Turku Turku Finland

Autonomous systems are becoming inherently ubiquitous with the advancements of computing and communication solutions enabling low-latency offloading and real-time collaboration of distributed devices. Decentralized technologies with blockchain and distributed ledger technologies (DLTs) are playing a key role. At the same time, advances in deep learning (DL) have significantly raised the degree of autonomy and level of intelligence of robotic and autonomous systems. While these technological revolutions were taking place, raising concerns in terms of data security and end-user privacy has become an inescapable research consideration. Federated learning (FL) is a promising solution to privacy-preserving DL at the edge, with an inherently distributed nature by learning on isolated data islands and communicating only model updates. However, FL by itself does not provide the levels of security and robustness required by today's standards in distributed autonomous systems. this survey covers applications of FL to autonomous robots, analyzes the role of DLT and FL for these systems, and introduces the key background concepts and considerations in current research. © 2021 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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A blockchain-based storage intelligent 18

A blockchain-based storage intelligent

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18th IEEE international Wireless Communications and Mobile computing conference (IWCMC)

作者： Jerbi, Wassim Cheikhrouhou, Omar Guermazi, Abderrahmen Hamam, Habib Trabelsi, Hafedh Univ Sfax CES Lab ENIS Sfax 3038 Tunisia Higher Inst Technol Studies Sfax Sfax Tunisia Univ Moncton Fac Engn Moncton NB E1A 3E9 Canada Univ Sfax Natl Sch Engn Sfax CES Lab Sfax Tunisia

ISBN: (纸本)9781665467490

A blockchain is a distributed and decentralized database that allows users to securely store and exchange data without requiring the intervention of a third party. Information, called transactions, is collected in blocks, which are then linked together via cryptographic processes in an irreversible way. the registry has served as a historical record of all actions taken by participants in the network since its launch. An increase in the number of transactions, leading to a considerable increase in the size of the primary blockchains, making them more difficult to maintain, perhaps discouraging certain nodes from storing the entire blockchain and therefore weakening decentralization. In this study, we introduce the low-storage node, a new type of node that stores chunks of blocks rather than whole blocks and is encoded with an erasure code. A low-storage node recovers the initial block by downloading and decoding a enough encoded fragments from other nodes in the network. this strategy has the advantage of allowing certain nodes to keep a reduced version of the blockchain while contributing to its decentralization. this simplifies the scaling of blockchains, which is one of the main flaws of the technology. BlockStock is a complete system that allows nodes to rent out its additional storage space to others. the main innovation is the use of blockchain-based smart contracts that enable frequent, automated and secure payments based on proofs of recovery provided by storage servers.

关键词： Decentralized Blockchain Storage data Consensus BlockStock Secure smart contract Ethereum

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