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Blockchain-enabled Edge computing Framework for Hierarchic Cluster-based Federated Learning

Blockchain-enabled Edge Computing Framework for Hierarchic C...

引用

international conference on Wireless Communications and Signal Processing (WCSP)

作者： Xiaoge Huang Yuhang Wu Zhi Chen Qianbin Chen Jie Zhang School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Communication and Information Engineering University of Sheffield United Kingdom

ISBN: (纸本)9781665450867

Federated learning implements decentralized machine learning tasks without exposing users' private data. However, in practical scenarios, intelligent devices data pertain to different fields are non-independent and identically distributed (non-IID), which leads to a decrease in the accuracy of the global model. In addition, if there are untrusted devices participated in federated learning, the global model accuracy will be decreased. To address the above-mentioned issues, in this paper, we propose a blockchain-enabled hierarchic cluster-based federated learning in edge computing framework to improve the accuracy of the global model and ensure the local model credibility. Firstly, we propose the hierarchic cluster-based federated learning (HCFL) algorithm, which realizes hierarchically aggregation based on user cosine similarity to improve global model accuracy. Moreover, blockchain technology is enabled in the proposed HCFL algorithm to verify the local model gradient from IDs before global aggregation. Moreover, incentive mechanism is proposed to dynamically adjust reward of IDs for promote IDs train trusted models. Finally, simulation results demonstrate the efficiency and performance of the blockchain-enabled hierarchic cluster-based federated learning framework.

关键词： Wireless communication Federated learning Computational modeling Simulation Heuristic algorithms Signal processing algorithms Clustering algorithms

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DEEP FEATURE WEIGHTING IN NAIVE BAYES FOR CHINESE TEXT CLASSIFICATION

DEEP FEATURE WEIGHTING IN NAIVE BAYES FOR CHINESE TEXT CLASS...

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2016 4th IEEE international conference on Cloud computing and Intelligence Systems（IEEE CCIS2016）

作者： Qiaowei Jiang Wen Wang Xu Han Shasha Zhang Xinyan Wang Cong Wang School of Software Engineering Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications School of Automation Beijing University of Posts and Telecommunications Air Force General Hospital

ISBN: (纸本)9781509012572

Naive Bayes(NB) continues to be one of the popular methods for text categorization because of its simplicity, efficiency and efficacy. In all of the existing feature weighting approaches, the learned feature weights are only applied to the classification of formula of Naive Bayes. In this paper, we propose a high efficient method which is called deep feature weighting Naive Bayes(DFWNB) [1]. DFWNB addresses to incorporate the learned weights into both the formula of classification and its conditional probability estimates. this paper defines the weight of each feature by TF-IDF feature weighting method. In the field of data mining, there are numerous studies about English text categorization but the Chinese text classification has received less attention from researchers. thus, we apply the deep feature weighting naive Bayes to Chinese text classifiers and obtain a better performance than ordinary feature weight naive Bayes(OFWNB).

关键词： Naive Bayes Deep feature weighting Chinese text classification TF-IDF

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Detection and Evaluation of Cybersecurity threats in MANET Based on AI

Detection and Evaluation of Cybersecurity Threats in MANET B...

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Ubiquitous computing and intelligent Information Systems (ICUIS), international conference on

作者： R. Mohandas Karthik Kumar Vaigandla N. Sivapriya Kirubasankar K Department of Electronics & Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu Department of Electronics & Communication Engineering Balaji Institute of Technology and Science Warangal Telangana Department of Computer Applications Cauvery College for Women Trichy Tamil Nadu

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

this research focuses on the use of AI to enhance the security of Mobile Ad-hoc Networks (MANETs), which aredecentralized and mobile wireless networks characterized by their dynamic nature. the work aims to assess the risks associated with cyber activities and identify various sorts of cyber threats, such as distributed Denial of Service (DDoS) attacks, malware invasions, data leaks and unauthorized access attempts. this will be achieved by using Artificial Intelligence (AI)-powered algorithms and models. the objective is to enhance the accuracy in identifying and categorizing these threats, hence increasing the security and dependability of MANET networks. Anomaly detection is a supplementary protection mechanism that specifically targets the hardware and network traffic of MANETs. Implementing a monitoring strategy is necessary in order to detect any anomalous activity that might potentially indicate security vulnerabilities or attacks in the MANET setups. the primary objective is to identify and address the unique characteristics of timely detection, which may enhance the security capabilities of MANET against cyber attacks. Machine learning (ML) algorithms often demonstrate impressive performance in efficiently identifying and accurately classifying various cyber threats, including DDoS attacks, malware infiltrations, and attempted unauthorized access. this anomaly detection technology effectively identifies robot abnormalities and malicious actions in network data, while also proactively avoiding system vulnerabilities or threats. In addition, this research discovered very effective AI-based cybersecurity solutions for dynamic decentralized MANET systems. these systems were specifically designed for tasks such as street-view switching, route finding, self healing, and self configuration.

关键词： Privacy Accuracy Machine learning Denial-of-service attack Ad hoc networks Malware Reliability Object recognition Mobile computing Anomaly detection

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Robust Categorical Data Clustering Guided by Multi-Granular Competitive Learning

Robust Categorical Data Clustering Guided by Multi-Granular ...

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international conference on distributed computing Systems

作者： Shenghong Cai Yiqun Zhang Xiaopeng Luo Yiu-Ming Cheung Hong Jia Peng Liu Guangdong University of Technology Guangzhou China Guangzhou Huali College Guangzhou China Hong Kong Baptist University Hong Kong SAR China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Data set composed of categorical features is very common in big data analysis tasks. Since categorical features are usually with a limited number of qualitative possible values, the nested granular cluster effect is prevalent in the implicit discrete distance space of categorical data. that is, data objects frequently overlap in space or subspace to form small compact clusters, and similar small clusters often form larger clusters. However, the distance space cannot be well-defined like the Euclidean distance due to the qualitative categorical data values, which brings great challenges to the cluster analysis of categorical data. In view of this, we design a Multi-Granular Competitive Penalization Learning (MGCPL) algorithm to allow potential clusters to interactively tune themselves and converge in stages with different numbers of naturally compact clusters. To leverage MGCPL, we also propose a Cluster Aggregation strategy based on MGCPL Encoding (CAME) to first encode the data objects according to the learned multi-granular distributions, and then perform final clustering on the embeddings. It turns out that the proposed MGCPL-guided Categorical Data Clustering (MCDC) approach is competent in automatically exploring the nested distribution of multi-granular clusters and highly robust to categorical data sets from various domains. Benefiting from its linear time complexity, MCDC is scalable to large-scale data sets and promising in pre-partitioning data sets or compute nodes for boosting distributed computing. Extensive experiments with statistical evidence demonstrate its superiority compared to state-of-the-art counterparts on various real public data sets.

关键词： Competitive learning distributed databases Clustering algorithms Euclidean distance Big Data Boosting Encoding

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Uncertain Investment Models for an Insurer with Ruin Constraint

Uncertain Investment Models for an Insurer with Ruin Constra...

引用

2012 IEEE 19th international conference on Industrial Engineering and Engineering Management(IE&EM 2012)

作者： Qing-feng Song Kai Shi School of Science Tianjin Institute of Urban Construction School of Computer Science and Communication Engineering Tianjin University of Technology

this paper focuses on the optimal investment proportion problem of insurance premium for an insurer in uncertain environments. Two uncertain investment models with ruin constraint are investigated, namely, an investment model with ruin constraint and constant per unit time premium (CPRCIM) and an investment model with ruin constraint and variable premium (VPRCIM), where the individual claim amounts are assumed as uncertain variables and the claim numerical processes are characterized as uncertain renewal processes. the equivalent forms of the above investment models are investigated, particularly, the expressions are given for normal distributed uncertain investment interest rate and lognormal distributed uncertain individual claim amount.

关键词： Investment Ruin Insurance Uncertain variable

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A Hypothesis Testing Using the Total Time on Test from Censored Data as Test Statistic

A Hypothesis Testing Using the Total Time on Test from Censo...

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the 2011 international conference on computing,Information and Control(ICCIC 2011)

作者： Shih-Chuan Cheng Department of Mathematics Creighton University

the total time on test for censored data from an exponentially distributed population has been proved to be a prediction sufficient(or adequate) statistic by Nair and Cheng [10] in light of the works by Cheng and Mordeson [4], and others [1, 9, 12-14]. the test may be repeated for many times. As a result, since the total time on test until the r-th ordered failure time is observed will be recorded for each test, several total time on test(for censored data) for the same system are available for use in analyzing the reliability of the system. the main objective of this proposal is to create a statistical test for the parameter of the exponential distribution on the basis of the total time on test from censored data.

关键词： Sufficient statistics Conditional independence Prediction sufficient statistics Factorization criterion Order statistics Censoring data Total time on test

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Dynamic look-ahead time in book-ahead reservation in QoS-enabled networks

Dynamic look-ahead time in book-ahead reservation in QoS-ena...

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IEEE international conference on Networks

作者： I. Ahmad J. Kamruzzaman S. Aswathanarayaniah Gippsland School of Computing and Information Technology Monash University Australia

In recent time book-ahead (BA) reservation has drawn increasing attention as multimedia and distributed applications become functionally richer. Book-ahead reservation has proved itself as an effective technique for time sensitive applications that require high amount of bandwidth with guaranteed QoS. One of the major concerns in the implementation of BA reservation in a QoS-enabled network is the preemption of on-going instantaneous requests (IR) calls. Preemption is a threat to service continuity which is seen as an important issue from user's perceived QoS definition found in recent studies. Earlier models proposed to reduce IR preemption use constant look-ahead time. this paper proposes a novel model to calculate look-ahead time dynamically taking traffic parameters and network state into consideration. Simulation results show that the proposed model achieves lower preemption probability, higher throughput, lower wasted throughput and higher effective revenue.

关键词： intelligent networks Quality of service throughput Bandwidth Telecommunication traffic Traffic control Video on demand Costs Admission control Computer networks

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IoT Based Smart Systems using Artificial Intelligence and Machine Learning: Accessible and intelligent Solutions

IoT Based Smart Systems using Artificial Intelligence and Ma...

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international conference on Information Systems and Computer Networks (ISCON)

作者： Bilal Ahmed Mirza Shuja Hari Mohan Mishra Ahmed Qtaishat Mukesh Kumar School of Computer Application Lovely Professional University Phagwara Punjab Department of Information Technology Sohar University Sohar Sultanate of Oman

the Internet of things (IoT) has grown in importance in both the technological and social spheres as consumers want smarter homes, more efficient businesses, and more efficient healthcare systems. the social setting is very important for this. Communication networks provide uninterrupted internet connectivity for both fixed and mobile devices like PCs and smartphones. Customers may now keep up their level of engagement while interacting with one or more apps at any time and from any location, thanks to developments in information and communication technologies. Many kinds of smart devices might potentially coordinate their responses to events, sending out alerts or keeping records of the system’s condition in line with a predetermined policy. Constant progress toward intelligent solutions is improving many facets of modern life, including manufacturing, transportation, electronic healthcare, electronic education, and many more. Machine-to-machine (M2M) communication in the IoT is autonomous and dynamic. Diverse approaches are being developed to address the challenges posed by disparate data sources, fluctuating signal quality, and overwhelming quantities of data. From the perspectives of intelligent system technologies, security, vulnerabilities, and the potential role that intelligent solutions based on Machine Learning (ML) and Artificial Intelligence (AI) may play, this article examines the IoT. Concerns about privacy and safety have arisen as a major obstacle to the widespread adoption of the Internet of things (IoT). We propose that the Standard Security Framework (SSF) be made mandatory for all distributed networks to ensure that hardware and software modules may communicate with one another regardless of their respective platforms. In addition, the effectiveness and security of new innovations in using fog and edge computing in conjunction with cloud applications have been studied.

关键词： Technological innovation Soft sensors Transportation Machine learning Safety Internet of things Security

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Transforming IoT with Federated Multimodal Learning: A Privacy-Centric Approach

Transforming IoT with Federated Multimodal Learning: A Priva...

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intelligent Technologies (CONIT), international conference on

作者： Ankita Suryavanshi Shiva Mehta Mukesh Kumar Siddhant thapliyal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Suppliers face significant challenges when discussing cloud-based health apps, such as implementing highly secure procedures and rigorous privacy safeguards. this applies to the fact that the acceptance of these applications is limited by the need for such quality of service, which is achieved mainly by reducing latency. this work is devoted to tackling these hurdles through the cutting-edge application of edge computing and model-federated learning aspects. Federated learning, a complex and distributed paradigm for machine learning helps perform computations near the data source and overcomes the constraint problem of a distant data source. Integrating the newest edge computing, our system includes an intelligent instrument that is applied to extract clinical data using Clustered Federated Learning (CFL). the value of this framework is evaluated using the performance metrics of two benchmark datasets, one of which represents different kinds of problems that occur in the day-to-day life of people, which concludes the effectiveness and significance of implementing machine learning at the edge in the health care sector the presented results above strongly suggest the fact that CFL promotes this model results in improvement. the performance indicators of the CFL model showed an appraisal boost with the increments in X-ray and Ultrasound datasets by 16% and 11%, respectively, when compared to those trained conventionally. Hence, edge AI through CFL can achieve the same tasks, and the possible scenarios can also be remarkably superior compared to a centralized data system.

关键词： COVID-19 Training Ultrasonic imaging Federated learning Soft sensors Medical services Quality of service

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A Particle Swarm Optimization based Feature Selection Method for Accident Severity Analysis

A Particle Swarm Optimization based Feature Selection Method...

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IEEE international conference on Cyber technology in Automation, Control, and intelligent Systems

作者： Chenye Qiu Xingquan Zuo Fei Xiang School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

Reducing accident severity is an effective mean to improve road safety level. Many researches have been done to identify the risky features which would influence the accident severity. Many risky features need to be considered when building accident severity analysis model, including driver, highway, vehicle, accident, and atmospheric factors. Some of those features are irrelevant of redundant. Using those features would decrease the performance of the prediction model and bring additional computational burden. However, there are very few researches on feature selection in accident severity analysis problem to date. In this paper, we propose a particle swarm optimization (PSO) based feature selection method for accident severity analysis. the proposed method can obtain a reduced number of feature subset from the original feature pool. In order to testify the method, the accident data of Beijing from 2008 to 2010 are used for experiment. Experimental results show the proposed PSO based feature selection method can significantly reduce the number of features while improving the classification accuracy. Moreover, it can provide better interpretation of the accident severity analysis model.

关键词： conferences Automation Control systems intelligent systems

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