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A Tutorial on Federated Learning from Theory to Practice:Foundations,software Frameworks,Exemplary Use Cases,and Selected Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 824-850页

作者： M.Victoria Luzón Nuria Rodríguez-Barroso Alberto Argente-Garrido Daniel Jiménez-López Jose M.Moyano Javier Del Ser Weiping Ding Francisco Herrera Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Communications Engineering University of the Basque Country(UPV/EHU)and also with TECNALIABasque Research&Technology Alliance(BRTA)Spain School of Information Science and Technology Nantong UniversityNantong 226019China

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third *** paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data *** virtue of FL,models can be learned from all such distributed data sources while preserving data *** aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software ***,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL *** ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

关键词： Data privacy distributed machine learning federated learning software frameworks

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A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation 13

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dy...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wu, Xunfeng Liu, Songbai Ji, Junkai Ma, Lijia Leung, Victor C. M. Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350308365

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases. © 2024 IEEE.

关键词： Digital elevation model

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Research on key technologies of edge cache in virtual data space across WAN

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Frontiers of computer science 2023年第1期17卷 15-34页

作者： Jiantong HUO Yaowen XU Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China College of Software Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China College of Computer Science and Technology Zhejiang UniversityHangZhou 310013China

The authors of this paper have previously proposed the global virtual data space system (GVDS) to aggregate the scattered and autonomous storage resources in China’s national supercomputer grid (National Supercomputing Center in Guangzhou, National Supercomputing Center in Jinan, National Supercomputing Center in Changsha, Shanghai Supercomputing Center, and computer Network Information Center in Chinese Academy of sciences) into a storage system that spans the wide area network (WAN), which realizes the unified management of global storage resources in China. At present, the GVDS has been successfully deployed in the China National Grid environment. However, when accessing and sharing remote data in the WAN, the GVDS will cause redundant transmission of data and waste a lot of network bandwidth resources. In this paper, we propose an edge cache system as a supplementary system of the GVDS to improve the performance of upper-level applications accessing and sharing remote data. Specifically, we first designs the architecture of the edge cache system, and then study the key technologies of this architecture: the edge cache index mechanism based on double-layer hashing, the edge cache replacement strategy based on the GDSF algorithm, the request routing based on consistent hashing method, and the cluster member maintenance method based on the SWIM protocol. The experimental results show that the edge cache system has successfully implemented the relevant operation functions (read, write, deletion, modification, etc.) and is compatible with the POSIX interface in terms of function. Further, it can greatly reduce the amount of data transmission and increase the data access bandwidth when the accessed file is located at the edge cache system in terms of performance, i.e., its performance is close to the performance of the network file system in the local area network (LAN).

关键词： virtual data space system wide area network edge cache redundant data transmission

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Semi-Supervised Cardiac Image Segmentation Using a Bicycle VAE with Cross Prior Attention

Semi-Supervised Cardiac Image Segmentation Using a Bicycle V...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Li, Shaojie Zhang, Yifan Yang, Xuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9781665468190

This paper proposes a semi-supervised learning framework for cardiac image segmentation based on a bicycle Variational Auto-Encoder (biVAE) architecture by embedding a Prior Transformer and a conditional generative network. Specifically, to enhance the presentation of extracted features, a Transformer with cross attention to prior is embedded in the encoder of our framework, where the global context and anatomical prior are modeled at each layer of the Prior Transformer's decoder to fuse multi-scale features. A biVAE is proposed by employing a conditional generative network, which maps the latent space to the image and maps the image to the latent space again. It makes the encoder of biVAE serve as a good feature representation for cardiac image segmentation. Many experiments on public cardiac MR datasets show that by introducing prior to the Transformer, more powerful features can be extracted in terms of representation, improving our semi-supervised framework's predictive performance. Furthermore, the proposed semi-supervised framework achieves competitive results with state-of-the-art supervised learning-based networks. © 2022 IEEE.

关键词： Image segmentation

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Route Optimization for Sailing Vessels using Artificial Intelligence Techniques 5

Route Optimization for Sailing Vessels using Artificial Inte...

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5th International Conference on Computational Intelligence and Intelligent Systems, CIIS 2022

作者： Anderson, John M. Sithungu, Siphesihle P. Ehlers, Elizabeth M. Academy of Computer Science and Software Engineering University of Johannesburg South Africa

ISBN: (纸本)9781450397612

Sailing is not a new concept. The use of artificial intelligence (AI) within the field of sailing however is a relatively new concept. AI is being applied to a variety of different aspects within the field of sailing such as AI plotting unmanned vessels across the globe as well as in the optimization of racing vessels in a variety of prestigious regatta. This paper aims to explore the application of AI to the route creation and plotting aspect of AI in sailing, the objective being to create a system that efficiently assists sailors with their route creation within a complex environment. The approach used is through the creation of a model that uses an adapted A∗(star) search algorithm where the weightings of the heuristic values used better represent the costs associated with the complex environment the algorithm operates in. The environment devised in this paper is a combination of geographical and meteorological data and coordinates to create a semi-dynamic environment in which the system operates. The resulting system successfully creates optimized routes within the semi-dynamic environment devised by the system. © 2022 ACM.

关键词： Artificial intelligence

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A Cyber Counterintelligence Competence Framework 21

A Cyber Counterintelligence Competence Framework

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21st European Conference on Cyber Warfare and Security, ECCWS 2022

作者： Sithole, Thenjiwe Du Toit, Jaco Academy of Computer Science and Software Engineering University of Johannesburg South Africa

ISBN: (纸本)9781914587405

The increased use of cyberspace and technological advancement are fundamentally changing the cyber threat landscape. Cyberattacks are becoming more sophisticated, frequent, and destructive. Internationally, there is a growing acceptance that Cyber Counterintelligence (CCI) is essential to counter cyber-attacks optimally. Therefore, in addition to government intelligence and security agencies, more companies are incorporating a CCI approach as a critical element of their posture for engaging cyber threats. However, the successful adoption of a CCI approach depends on the availability of skilled CCI professionals equipped with the requisite competences. The creation of such CCI professionals, in turn, requires a framework for developing the necessary CCI competences. At least in as far as reviewed academic literature is concerned, there is no existing postulation on a framework to develop the CCI competences, specifically for developing countries. Given the complexity and multi-disciplinary nature of the emerging CCI field, such a framework needs to provide two distinctive skillsets linked to CCI’s two distinct areas of expertise, namely cyber (security) and counterintelligence. The paper presents a high-level Cyber Counterintelligence Competence Framework (CCIC Framework) that outlines dimensions of CCI, functional areas, job roles and requisite competences (knowledge, skills, and abilities), and tasks for each CCI job role. The CCI framework also outlines five levels of proficiency expected for each job role. The identification of competences and levels of proficiency are integral to the successful implementation of the framework and workforce development. The CCIC Framework is intended to be used as a tool to retain, assess, and monitor knowledge, skills, and abilities for CCI workforce development. In addition, the CCIC Framework can be used to assist in providing the basis for individual performance management, education, training, and development pathway, as wel

关键词： Cybersecurity

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Effective Cyber Threat Hunting: Where and how does it fit? 21

Effective Cyber Threat Hunting: Where and how does it fit?

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21st European Conference on Cyber Warfare and Security, ECCWS 2022

作者： Ntingi, Nombeko Duvenage, Petrus du Toit, Jaco von Solms, Sebastian Academy of Computer Science and Software Engineering University of Johannesburg South Africa

ISBN: (纸本)9781914587405

Traditionally threat detection in organisations is reactive through pre-defined and preconfigured rules that are embedded in automated tools such as firewalls, anti-virus software, security information and event management (SIEMs) and intrusion detection systems/intrusion prevention systems (IDS/IPS). As the fourth industrial revolution (4IR) brings with it an exponential increase in technological advances and global interconnectivity, the cyberspace presents security risks and threats the scale of which is unprecedented. These security risks and threats have the potential of exposing confidential information, damaging the reputation of credible organisations and/or inflicting harm. The regular occurrence and complexity of cyber intrusions makes the guarding enterprise and government networks a daunting task. Nation states and businesses need to be ingenious and consider innovative and proactive means of safeguarding their valuable assets. The growth of technological, physical and biological worlds necessitates the adoption of a proactive approach towards safeguarding cyber space. This paper centers on cyber threat hunting (CTH) as one such proactive and important measure that can be adopted. The paper has a central contention that effective CTH cannot be an autonomous ‘plug in’ or a standalone intervention. To be effective CTH has to be synergistically integrated with relevant existing fields and practices. Academic work on such conceptual integration of where CTH fits is scarce. Within the confines of the paper we do not attempt to integrate CTH with many of the various relevant fields and practices. Instead, we limit the scope to postulations on CTH’s interface with two fields of central importance in cyber security, namely Cyber Counterintelligence (CCI) and Cyber Threat Monitoring and Analysis (CTMA). The paper’s corresponding two primary objectives are to position CTH within the broader field of CCI and further contextualise CTH within the CTMA domain. The pos

关键词： Cybersecurity

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Multi-Stage Transfer Learning Evolutionary Algorithm for Dynamic Multiobjective Optimization 13

Multi-Stage Transfer Learning Evolutionary Algorithm for Dyn...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wang, Qianhui Zhu, Qingling Ji, Junkai College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

ISBN: (纸本)9798350308365

Recently, the application of transfer learning within dynamic multiobjective evolutionary algorithms (DMOEAs) has shown significant potential to solve dynamic multiobjective optimization problems (DMOPs). This approach utilizes the transfer learning technique which reuses information from previous environments to accelerate the search process in the new environment. However, the risk of negative transfer can lead to an erroneous search direction and inefficient use of computational resources. To address this issue, this paper proposes a novel multistage transfer learning DMOEA, named MSTL. The algorithm is designed to enhance the convergence and diversity of the population through the combination of the pre-transfer learning stage and the transfer learning and validation stage when environmental changes occur. In the pre-transfer learning stage, a suitable target domain, source domain, and validation classifier sample sets are generated guided by historical information. In the transfer learning and validation stage, the TrAdaboost technique to transfer knowledge from the target to the source domain, with the results verified by two validation classifiers to mitigate negative transfer. Experimental results demonstrate that our proposed algorithm outperforms four competing algorithms in terms of diversity and convergence on a widely recognized benchmark suite. © 2024 IEEE.

关键词： Dynamic multiobjective optimization evolutionary algorithm transfer learning

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Secret Sharing Based Key Agreement Protocol for Body Area Networks 18th

Secret Sharing Based Key Agreement Protocol for Body Area N...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Sheng, Weihong Cai, Bin Hu, Chunqiang Li, Ruinian School of Big Data and Software Engineering Chongqing University Chongqing China Computer Science Department Bowling Green State University Bowling Green United States

ISBN: (纸本)9783031714634

In order to provide more comprehensive medical services and personalized health monitoring according to individual needs, Body Area Networks (BANs) have been extensively studied by many researchers. As BANs involve the transmission of personal private data, the security of the communication is of utmost importance. Unfortunately, existing encryption techniques cannot be directly applied with the limited power or computation ability of the sensors in BANs. An alternative direction is to use physiological signals for key agreement. However, many of the current physiological signal-based key agreement schemes either have high overheads or are vulnerable to certain security issues. In this paper, we proposed a novel secret sharing and Bloom filter based key agreement scheme that balances overhead and security for BANs. Also, we use an approximate hash table to free ourselves from the problem of dependence on the ordering of features, which has been neglected by previous researchers. We validate the security of our scheme with based on real datasets and compare overheads with other solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Image-based Jointly Sparse Maximum Margin Criterion with Pairwise Constrain for Feature Extraction 2

Image-based Jointly Sparse Maximum Margin Criterion with Pai...

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2nd International Conference on computer science, Electronic Information engineering and Intelligent Control Technology, CEI 2022

作者： Zhou, Yifan The College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781665476164

ISBN: (纸本)9781665476164

In this paper, we propose an image-based feature extraction framework. To our best knowledge, there are few methods to combine image feature extraction and pairwise constrain (PC) jointly and directly. We fill the gap between them. With the PC, we devise an objective function, taking the PC and image feature extraction consideration jointly. Besides, we introduce maximum margin criterion into the loss function, making all positive constrains and negative constrains take part in the process of feature extraction. Finally, the solution of the new framework can be deduced into two sperate individual problems. Comprehensive experiment results show our framework effective. © 2022 IEEE.

关键词： Feature extraction

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