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A Survey of Federated Learning:Advances in Architecture,Synchronization,and Security Threats 认领 引用
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作者 Faisal Mahmud Fahim Mahmud Rashedur M.Rahman 《Computers, Materials & Continua》 SCIE EI 2026年第3期1-87,共87页
Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitiv... Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption. 展开更多
关键词 Federated learning(FL) horizontal federated learning(HFL) vertical federated learning(VFL) federated transfer learning(FTL) personalized federated learning synchronous federated learning(SFL) asynchronous federated learning(AFL) data leakage poisoning attacks privacy-preserving machine learning
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Machine Learning for NTN-Assisted IoT:A Bibliometric-Assisted Survey of Optimization across Trajectory,Resource,Energy,and Security Aspects 认领 引用
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作者 Oluwatosin Ahmed Amodu Zurina Mohd Hanapi +5 位作者 Chedia Jarray Huda Althumali Faten A.Saif Raja Azlina Raja Mahmood Mohammed Sani Adam Nor Fadzilah Abdullah 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期168-235,共68页
Non-terrestrial networks(NTNs)—including UAVs,HAPs,and satellite systems—are rapidly becoming key enablers of wide-area,resilient connectivity for large-scale IoT applications.As these platforms integrate with terre... Non-terrestrial networks(NTNs)—including UAVs,HAPs,and satellite systems—are rapidly becoming key enablers of wide-area,resilient connectivity for large-scale IoT applications.As these platforms integrate with terrestrial networks to form space-air-ground architectures,optimization challenges related to trajectory,resource management,energy efficiency,and security become increasingly complex.Machine learning(ML)has emerged as a central tool for addressing these challenges by enabling adaptive,data-driven decision-making under uncertainty.This survey presents an optimization-centric review of ML-based NTN-assisted IoT systems focusing on aspect-specific datasets.Using a structured methodology involving dataset curation,keyword filtering,metadata analysis,and citationbased paper selection,we analyze representative and influential works across four core optimization themes:trajectory planning,resource allocation,energy utilization,and security.We develop a taxonomy that captures problem types,learning approaches,architectural configurations,and cross-layer constraints,and discuss insights,complemented by a focused review of top-cited contributions in each theme as well as discussions relating to their complexities and practicality.Our analysis reveals clear methodological trends,including the growing use of deep and multiagent reinforcement learning,the emergence of distributed intelligence through federated learning,and the increasing interplay among mobility,computation,communication,resource allocation,energy optimization and security.Finally,we highlight key lessons and future research opportunities related to scalable cooperative learning,energy-efficient operation,secure distributed intelligence,and multi-tier optimization across space-air-ground integrated networks,offering a roadmap toward resilient and intelligent 6G-era connectivity. 展开更多
关键词 Machine learning deep learning reinforcement learning deep reinforcement learning satellites unmanned aerial vehicles drones altitude platforms Internet of Things wireless sensor networks
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pFedUL:Layer-Aware Federated Unlearning for Personalized Federated Learning 认领 引用
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作者 Zhuodong Liu Xiangyu Li Zhihao Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第9期225-252,共28页
Federated unlearning(FU)enables the removal of specific data contributions from federated learning(FL)models to comply with regulations such as the General Data Protection Regulation(GDPR).However,most existing FU met... Federated unlearning(FU)enables the removal of specific data contributions from federated learning(FL)models to comply with regulations such as the General Data Protection Regulation(GDPR).However,most existing FU methods are designed for the FedAvg paradigm,where all clients share a single global model.In practice,personalized federated learning(pFL)methods such as FedPer,FedRep,Ditto,and FedBN have become widely adopted due to their superior handling of non-IID data.These methods decompose the model into shared global layers and client-specific personalized layers,fundamentally altering the semantics of unlearning,yet this setting has received little attention.We formalize FU under the pFL paradigm,identifying a tension between unlearning completeness on shared layers and personalization preservation for remaining clients.We then propose pFedUL,a layer-aware selective unlearning framework comprising three components:(1)gradient-based layer-wise contribution attribution that separately quantifies the target client’s influence on shared and personalized parameters,(2)adaptive selective unlearning that applies differentiated forgetting strategies across layer types,and(3)a lightweight recalibration protocol enabling remaining clients to restore personalization with minimal overhead.We further introduce two new metrics,Personalization Preservation Score(PPS)and Cross-client Fairness Index(CFI),to evaluate pFL-specific unlearning quality.Experiments on CIFAR-10,CIFAR-100,and FEMNIST under varying non-IID settings indicate that pFedUL achieves unlearning effectiveness comparable to full retraining while maintaining an average of 97.3%personalized accuracy for remaining clients.Compared with six state-of-the-art FU methods adapted to the pFL setting,pFedUL consistently achieves superior personalization preservation,improving over the best existing method by 6.3%in PPS on average with an 8.4×speedup,averaged across all tested pFL architectures and datasets. 展开更多
关键词 Federated learning federated unlearning personalized federated learning machine unlearning layeraware unlearning right to be forgotten
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Mitigating Attribute Inference in Split Learning via Channel Pruning and Adversarial Training 认领 引用
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作者 Afnan Alhindi Saad Al-Ahmadi Mohamed Maher Ben Ismail 《Computers, Materials & Continua》 SCIE EI 2026年第3期1465-1489,共25页
Split Learning(SL)has been promoted as a promising collaborative machine learning technique designed to address data privacy and resource efficiency.Specifically,neural networks are divided into client and server subn... Split Learning(SL)has been promoted as a promising collaborative machine learning technique designed to address data privacy and resource efficiency.Specifically,neural networks are divided into client and server subnetworks in order to mitigate the exposure of sensitive data and reduce the overhead on client devices,thereby making SL particularly suitable for resource-constrained devices.Although SL prevents the direct transmission of raw data,it does not alleviate entirely the risk of privacy breaches.In fact,the data intermediately transmitted to the server sub-model may include patterns or information that could reveal sensitive data.Moreover,achieving a balance between model utility and data privacy has emerged as a challenging problem.In this article,we propose a novel defense approach that combines:(i)Adversarial learning,and(ii)Network channel pruning.In particular,the proposed adversarial learning approach is specifically designed to reduce the risk of private data exposure while maintaining high performance for the utility task.On the other hand,the suggested channel pruning enables the model to adaptively adjust and reactivate pruned channels while conducting adversarial training.The integration of these two techniques reduces the informativeness of the intermediate data transmitted by the client sub-model,thereby enhancing its robustness against attribute inference attacks without adding significant computational overhead,making it wellsuited for IoT devices,mobile platforms,and Internet of Vehicles(IoV)scenarios.The proposed defense approach was evaluated using EfficientNet-B0,a widely adopted compact model,along with three benchmark datasets.The obtained results showcased its superior defense capability against attribute inference attacks compared to existing state-of-the-art methods.This research’s findings demonstrated the effectiveness of the proposed channel pruning-based adversarial training approach in achieving the intended compromise between utility and privacy within SL frameworks.In fact,the classification accuracy attained by the attackers witnessed a drastic decrease of 70%. 展开更多
关键词 Split learning privacy-preserving split learning distributed collaborative machine learning channel pruning adversarial learning resource-constrained devices
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A Cooperative Hybrid Learning Framework for Automated Dandruff Severity Grading 认领 引用
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作者 Sin-Ye Jhong Hui-Che Hsu +3 位作者 Hsin-Hua Huang Chih-Hsien Hsia Yulius Harjoseputro Yung-Yao Chen 《Computers, Materials & Continua》 SCIE EI 2026年第4期2272-2285,共14页
Automated grading of dandruff severity is a clinically significant but challenging task due to the inherent ordinal nature of severity levels and the high prevalence of label noise from subjective expert annotations.S... Automated grading of dandruff severity is a clinically significant but challenging task due to the inherent ordinal nature of severity levels and the high prevalence of label noise from subjective expert annotations.Standard classification methods fail to address these dual challenges,limiting their real-world performance.In this paper,a novel,three-phase training framework is proposed that learns a robust ordinal classifier directly from noisy labels.The approach synergistically combines a rank-based ordinal regression backbone with a cooperative,semi-supervised learning strategy to dynamically partition the data into clean and noisy subsets.A hybrid training objective is then employed,applying a supervised ordinal loss to the clean set.The noisy set is simultaneously trained using a dualobjective that combines a semi-supervised ordinal loss with a parallel,label-agnostic contrastive loss.This design allows themodel to learn fromthe entire noisy subset while using contrastive learning to mitigate the risk of error propagation frompotentially corrupt supervision.Extensive experiments on a new,large-scale,multi-site clinical dataset validate our approach.Themethod achieves state-of-the-art performance with 80.71%accuracy and a 76.86%F1-score,significantly outperforming existing approaches,including a 2.26%improvement over the strongest baseline method.This work provides not only a robust solution for a practical medical imaging problem but also a generalizable framework for other tasks plagued by noisy ordinal labels. 展开更多
关键词 Dandruff severity grading ordinal regression noisy label learning self-supervised learning contrastive learning medical image analysis
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A Two-Timescale Neurodynamic Approach to Sharpness-Aware Minimization in Deep Learning 认领 引用
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作者 Dan Su Jun Wang +1 位作者 Chunhua Yang Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1530-1532,共3页
Dear Editor,This letter presents a two-timescale neurodynamic algorithm for sharpness-aware minimization in deep learning.Deep learning achieves remarkable success in areas such as computer vision,natural language pro... Dear Editor,This letter presents a two-timescale neurodynamic algorithm for sharpness-aware minimization in deep learning.Deep learning achieves remarkable success in areas such as computer vision,natural language processing,robotics and control.In deep learning,it is essential to boost their generalization power[1].Existing deep learning strategies for improving the generalization power include regularization,data augmentation,etc.[2]. 展开更多
关键词 deep learning computer visionnatural language processingrobotics generalization power boost their deep learningit deep learningdeep learning two timescale neurodynamic approach sharpness aware minimization
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Temporal Dependency-Aware Trajectory-Level Behavioural Metric for Exploration in Reinforcement Learning 认领 引用
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作者 Anjie Zhu Yongjun Yang +1 位作者 Guangyi Zhao Jie Shao 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第2期332-348,共17页
Intrinsic motivation serves as the predominant paradigm of exploration in reinforcement learning.In pursuit of an informative and robust state representation,the behavioural metric groups behaviourally equivalent stat... Intrinsic motivation serves as the predominant paradigm of exploration in reinforcement learning.In pursuit of an informative and robust state representation,the behavioural metric groups behaviourally equivalent states together,which share the same single-step reward and transition distribution.However,due to the presence of uninformative rewards and the dynamic nature of procedurally generated environments,these behavioural metric-based approaches could limit the effectiveness of the learnt state representations,potentially leading to a representation collapse and an ineffective exploration.Therefore,a more comprehensive and generalisable behavioural metric is needed to overcome the above issues.In this work,we approach the exploration problem from a novel perspective,extending beyond the conventional single-step assessments to encompass a longterm consideration of the whole trajectory.Specifically,we propose a novel trajectory-level behavioural metric(TBM)that exploits temporal dependencies of the trajectory and captures the underlying sequential information of behaviour patterns.To achieve an effective trajectory representation for exploration,we develop a pivotal state identifier(PSI)and a trajectory return estimator(TRE)to distinguish the diverse contributions of individual states in the trajectory.Moreover,an auxiliary representation regulariser is developed to promote the diversity and informativeness of the trajectory representation,mitigating the risk of representation mode collapse.Extensive experiments and empirical analysis conducted on procedurally generated environments showcase the superior performance of our proposed framework. 展开更多
关键词 learning(artificial intelligence) machine learning reinforcement learning
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FSL-TM:Review on the Integration of Federated Split Learning with TinyML in the Internet of Vehicles 认领 引用
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作者 Meenakshi Aggarwal Vikas Khullar Nitin Goyal 《Computers, Materials & Continua》 SCIE EI 2026年第2期290-320,共31页
The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects.... The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects.IoV systems typically send massive volumes of raw data to central servers,which may raise privacy issues.Additionally,model training on IoV devices with limited resources normally leads to slower training times and reduced service quality.We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning(TinyML)approach,which operates on IoV edge devices without sharing sensitive raw data.Specifically,we focus on integrating split learning(SL)with federated learning(FL)and TinyML models.FL is a decentralisedmachine learning(ML)technique that enables numerous edge devices to train a standard model while retaining data locally collectively.The article intends to thoroughly discuss the architecture and challenges associated with the increasing prevalence of SL in the IoV domain,coupled with FL and TinyML.The approach starts with the IoV learning framework,which includes edge computing,FL,SL,and TinyML,and then proceeds to discuss how these technologies might be integrated.We elucidate the comprehensive operational principles of Federated and split learning by examining and addressingmany challenges.We subsequently examine the integration of SL with FL and various applications of TinyML.Finally,exploring the potential integration of FL and SL with TinyML in the IoV domain is referred to as FSL-TM.It is a superior method for preserving privacy as it conducts model training on individual devices or edge nodes,thereby obviating the necessity for centralised data aggregation,which presents considerable privacy threats.The insights provided aim to help both researchers and practitioners understand the complicated terrain of FL and SL,hence facilitating advancement in this swiftly progressing domain. 展开更多
关键词 Machine learning federated learning split learning TinyML internet of vehicles
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Learning from Scarcity:A Review of Deep Learning Strategies for Cold-Start Energy Time-Series Forecasting 认领 引用
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作者 Jihoon Moon 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期26-76,共51页
Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-iti... Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-ities where operational records are scarce.This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such“cold-start”forecasting problems.It primarily covers three interrelated domains—solar photovoltaic(PV),wind power,and electrical load forecasting—where data scarcity and operational variability are most critical,while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective.To this end,we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025,highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data.Moreover,transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new,data-sparse environments,effectively capturing hidden dependencies among geographic features,meteorological dynamics,and grid structures.Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios.In addition,large language models and explainable artificial intelligence(XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms,fostering operator trust from the earliest deployment stages.By consolidating methodological advances,unresolved challenges,and open-source resources,this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient,low-carbon electricity grids. 展开更多
关键词 Cold-start forecasting zero-shot learning few-shot meta-learning transfer learning spatiotemporal graph neural networks energy time series large language models explainable artificial intelligence(XAI)
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Quantum machine learning for smart healthcare applications 认领 引用
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作者 Abdul Ahad 《Medical Data Mining》 CAS 2026年第3期1-3,共3页
Introduction Quantum machine learning(QML)is emerging as a revolutionary tool in the healthcare industry,promising to enhance data processing capabilities and improve outcomes.By merging the computational power of qua... Introduction Quantum machine learning(QML)is emerging as a revolutionary tool in the healthcare industry,promising to enhance data processing capabilities and improve outcomes.By merging the computational power of quantum computing with machine learning models,QML can address complex challenges such as drug discovery,personalized medicine,and diagnostic accuracy.This article explores the applications,benefits,and challenges of integrating QML into smart healthcare,shedding light on its transformative potential. 展开更多
关键词 quantum machine learning qml quantum machine learning quantum computing enhance data processing capabilities machine learning modelsqml diagnostic accuracythis transformative potentia drug discoverypersonalized medicineand
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An Improved Reinforcement Learning-Based 6G UAV Communication for Smart Cities 认领 引用 被引量：1
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作者 Vi Hoai Nam Chu Thi Minh Hue Dang Van Anh 《Computers, Materials & Continua》 SCIE EI 2026年第1期2030-2044,共15页
Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic top... Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments. 展开更多
关键词 UAV FANET smart cities reinforcement learning Q-learning
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HPoolGCL:Augmentation-Free Cross-Granularity Graph Contrastive Learning With Hierarchical Pooling 认领 引用
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作者 Fenglin Cen Shijie Liu +2 位作者 Quan Feng Tiantian He Ji Xu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第2期610-632,共23页
Graph contrastive learning(GCL)has emerged as a dominant paradigm for self-supervised representation learning for attributed graph data.However,existing GCL methods heavily rely on empirical graph data augmentation,wh... Graph contrastive learning(GCL)has emerged as a dominant paradigm for self-supervised representation learning for attributed graph data.However,existing GCL methods heavily rely on empirical graph data augmentation,which may distort intrinsic graph semantics and produce poor generalisation without carefully chosen or designed augmentation techniques.Furthermore,most GCL approaches focus on same-granularity contrastive learning(e.g.,node vs.node),neglecting the hierarchical and multigranular properties inherent in real-world networks,leading to suboptimal performance.To address these limitations,we propose HPoolGCL,a cross-granularity GCL framework compatible with various hierarchical graph pooling methods to capture multigranularity information.Our framework eliminates the need for handcrafted augmentations,explicit negative sampling and complex multiencoder architectures by applying two novel loss functions in hierarchical graph pooling.The theoretical analysis is provided to explain the effectiveness of unified MGC and HiCR losses from three perspectives,namely,the information maximisation principle,the redundancy reduction principle and the information bottleneck principle.The experimental results demonstrate that HPoolGCL achieves state-of-the-art performance across multiple downstream tasks on five benchmarks.Our codes are available at http://gffzz188fe103f8f1460asfvobxknfq50k6ob6.ffgz.tsg.suse.edu.cn/Heycen/HPoolGCL. 展开更多
关键词 data mining deep learning machine learning neural network unsupervised learning
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Multi-Objective Optimisation Framework for Heterogeneous Federated Learning 认领 引用
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作者 Jamshid Tursunboev Vikas Palakonda +2 位作者 Il-Min Kim Sunghwan Moon Jae-Mo Kang 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第1期1-14,共14页
Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces s... Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces several unsolved challenges.Specifically,communication costs and system heterogeneity,such as nonidentical data distribution,hinder federated learning's progress.Several approaches have recently emerged for federated learning involving heterogeneous clients with varying computational capabilities(namely,heterogeneous federated learning).However,heterogeneous federated learning faces two key challenges:optimising model size and determining client selection ratios.Moreover,efficiently aggregating local models from clients with diverse capabilities is crucial for addressing system heterogeneity and communication efficiency.This paper proposes an evolutionary multiobjective optimisation framework for heterogeneous federated learning(MOHFL)to address these issues.Our approach elegantly formulates and solves a biobjective optimisation problem that minimises communication cost and model error rate.The decision variables in this framework comprise model sizes and client selection ratios for each Q client cluster,yielding a total of 2×Q optimisation parameters to be tuned.We develop a partition-based strategy for MOHFL that segregates clients into clusters based on their communication and computation capabilities.Additionally,we implement an adaptive model sizing mechanism that dynamically assigns appropriate subnetwork architectures to clients based on their computational constraints.We also propose a unified aggregation framework to combine models of varying sizes from heterogeneous clients effectively.Extensive experiments on multiple datasets demonstrate the effectiveness and superiority of our proposed method compared to existing approaches. 展开更多
关键词 deep learning learning(artificial intelligence) learning models multi-objective optimisation
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Privacy-Preserving Phishing Detection:A Systematic Review of LLMs,Federated Learning,and Blockchain Integration 认领 引用
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作者 Ghadi Almaktoom Suliman Aladhadh Salim El Khediri 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期236-269,共34页
The rapid growth of phishing attempts in the enterprise could potentially lead to bankruptcy.The primary focus of the research is on detecting phishing attacks,with no interest in how the data is processed.Attackers u... The rapid growth of phishing attempts in the enterprise could potentially lead to bankruptcy.The primary focus of the research is on detecting phishing attacks,with no interest in how the data is processed.Attackers use fraudulent methods to obtain valuable,confidential information,resulting in billions of dollars in financial losses for enterprises.In our review,we examined the methods used in phishing-detection studies.We concluded that the two main sections,centralized and decentralized methods,were the centralized ones,which aggregate data in a central server and thus violate data protection regulations,such as GDPR.In order to properly investigate the field,we put four main questions to give the reader a proper understanding of the field:what are the major detection approaches,what are their limitations and gaps,which datasets are most commonly used and trusted across different studies,and which privacy-preserving detection approaches are used and investigated in the field of phishing detection.To address these questions,we examined 105 different papers published from 2015 to 2024.Our review covers machine learning,deep learning,hybrid methods,large language models(LLMs),federated learning,and blockchain-based detection.Our investigation led to centralized approaches that achieved more than 95%accuracy but raised privacy concerns.Keeping data local on user devices offers privacy protection,as in decentralized strategies such as federated learning,at the cost of an accuracy trade-off of 1%3%.Other decentralized methods,such as blockchain-based systems,enhance security and transparency in the pricing of computational challenges. 展开更多
关键词 Phishing detection federated learning blockchain privacy-preserving deep learning BERT natural language processing smart contracts decentralized machine learning cybersecurity
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A Hybrid Approach to Software Testing Efficiency:Stacked Ensembles and Deep Q-Learning for Test Case Prioritization and Ranking 认领 引用
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作者 Anis Zarrad Thomas Armstrong Jaber Jemai 《Computers, Materials & Continua》 SCIE EI 2026年第3期1726-1746,共21页
Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for opti... Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies. 展开更多
关键词 Software testing test case prioritization test case ranking machine learning reinforcement learning deep Q-learning
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A primer on adult learning theory for ophthalmology education:foundations and narrative review 认领 引用
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作者 Sophia Y.Choi Savannah Nordin +1 位作者 Jennifer Dunnigan Nailyn Rasool 《Annals of Eye Science》 2026年第1期32-39,共8页
Background and Objective:Adult learning theory(ALT)is rooted in the fundamental differences in how adults learn as compared to children.ALT is referred to as andragogy and is distinct from how children learn,known as ... Background and Objective:Adult learning theory(ALT)is rooted in the fundamental differences in how adults learn as compared to children.ALT is referred to as andragogy and is distinct from how children learn,known as pedagogy.The unique needs of adult learners stem from their motivation to learn,desire for self-direction of knowledge,prior experiences,sense of responsibility,and a problem-solving mindset.These factors are critical considerations when building programs for residency training and medical education.Specifically in ophthalmology,given its highly detailed,technologically advanced,and intricate nature,the implementation of ALT principles throughout the trainee’s career is fundamental to building a foundation for lifelong continuous learning.The purpose of this article is to provide a foundation on ALT and present evidence-based methods that ALT can be used to improve ophthalmology education.Methods:A literature search was conducted using PubMed and Scopus databases,including articles from 2015 to 2025.Included were original studies,reviews,and peer-reviewed articles in English relevant to ALT principles and involving ophthalmology trainees,educators,or teaching methods.Study screening was conducted by two independent authors(S.Y.C.and S.N.)using systematic review tool Covidence.Key Content and Findings:This article presents the foundations of ALT and an overview of the implementation of ALT principles into ophthalmology education and training.The goal of this review is to explore successful strategies in ALT implementation in ophthalmology as well as barriers to implementation.Conclusions:Implementation of ALT into ophthalmology education using various methods has been found efficacious compared to traditional learning methods.Barriers to implementation,such as institutional philosophy,faculty confidence and availability,and resource allocations,must be addressed to effectively introduce ALT into educational programs. 展开更多
关键词 Ophthalmology ophthalmology education adult learning theory(ALT) self-directed learning(SDL) experiential learning
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Distributed Iterative Learning Model Predictive Control for Unmanned Surface Vehicles 认领 引用
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作者 Xinyuan Huang Lihao Ma Huiping Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1512-1514,共3页
Dear Editor,This letter proposes a distributed iterative learning model predictive control(LMPC)strategy for coordinated trajectory tracking of multiple unmanned surface vehicles(USVs).By learning from previously feas... Dear Editor,This letter proposes a distributed iterative learning model predictive control(LMPC)strategy for coordinated trajectory tracking of multiple unmanned surface vehicles(USVs).By learning from previously feasible control and state trajectories,each USV iteratively refines its input sequence to improve the accuracy of trajectory tracking and formation control.To tackle challenges such as system coupling,limited onboard computational resources,and communication constraints,the method integrates the alternating direction method of multipliers(ADMM)with iterative learning.The effectiveness and advantages of the proposed approach are demonstrated through comparison results. 展开更多
关键词 Distributed Control learning previously feasible control state trajectorieseach distributed iterative learning model predictive control lmpc strategy unmanned surface vehicles usvs coordinated trajectory tracking Iterative Learning Control Model Predictive Control Unmanned Surface Vehicles
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A State-of-the-Art Survey of Adversarial Reinforcement Learning for IoT Intrusion Detection 认领 引用
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作者 Qasem Abu Al-Haija Shahad Al Tamimi 《Computers, Materials & Continua》 SCIE EI 2026年第4期26-94,共69页
Adversarial Reinforcement Learning(ARL)models for intelligent devices and Network Intrusion Detection Systems(NIDS)improve systemresilience against sophisticated cyber-attacks.As a core component of ARL,Adversarial Tr... Adversarial Reinforcement Learning(ARL)models for intelligent devices and Network Intrusion Detection Systems(NIDS)improve systemresilience against sophisticated cyber-attacks.As a core component of ARL,Adversarial Training(AT)enables NIDS agents to discover and prevent newattack paths by exposing them to competing examples,thereby increasing detection accuracy,reducing False Positives(FPs),and enhancing network security.To develop robust decision-making capabilities for real-world network disruptions and hostile activity,NIDS agents are trained in adversarial scenarios to monitor the current state and notify management of any abnormal or malicious activity.The accuracy and timeliness of the IDS were crucial to the network’s availability and reliability at this time.This paper analyzes ARL applications in NIDS,revealing State-of-The-Art(SoTA)methodology,issues,and future research prospects.This includes Reinforcement Machine Learning(RML)-based NIDS,which enables an agent to interact with the environment to achieve a goal,andDeep Reinforcement Learning(DRL)-based NIDS,which can solve complex decision-making problems.Additionally,this survey study addresses cybersecurity adversarial circumstances and their importance for ARL and NIDS.Architectural design,RL algorithms,feature representation,and training methodologies are examined in the ARL-NIDS study.This comprehensive study evaluates ARL for intelligent NIDS research,benefiting cybersecurity researchers,practitioners,and policymakers.The report promotes cybersecurity defense research and innovation. 展开更多
关键词 Reinforcement learning network intrusion detection adversarial training deep learning cybersecurity defense intrusion detection system and machine learning
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PhytoCell:An ensemble learning framework for identifying cell states in plant scRNA-seq data 认领 引用
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作者 Hao Wang Shen Yan +10 位作者 Xiaoding Ma Huan Si Qiong Lu Yanqing Chen Lijia Liu Jingpeng Hong Xingjian Xu Wei Fang Qiang He Yanjun Zan Aiguo Yang 《The Crop Journal》 SCIE CSCD 2026年第4期1364-1373,共10页
Single-cell transcriptome sequencing(scRNA-seq)can reveal the roles of diverse cells in an organism,but accurately classifying cell subpopulations and their marker genes remains a challenge.Here,we present PhytoCell,a... Single-cell transcriptome sequencing(scRNA-seq)can reveal the roles of diverse cells in an organism,but accurately classifying cell subpopulations and their marker genes remains a challenge.Here,we present PhytoCell,an ensemble learning framework that combines feature selection engineering with machine learning to uncover cell markers and annotate cell subpopulations.We evaluated our approach on 120,000 cells from corollas of the dicotyledonous plant species coyote tobacco(Nicotiana attenuata)and eight tissues from the monocotyledonous plant species rice(Oryza sativa).Comprehensive evaluation across species and tissues demonstrated that PhytoCell effectively eliminates redundant information,identifies key cell markers,improves clustering performance,and accurately classifies cell subpopulations.Importantly,PhytoCell did not rely on prior biological knowledge for selecting cell markers,preserving the biological landscape of the original data.For broader accessibility,we developed a userfriendly web interface that provides convenient tools for users to access cell marker resources and perform predictions for cell type.PhytoCell is freely accessible at http://gffzz51747b2a50cc4558sfvobxknfq50k6ob6.ffgz.tsg.suse.edu.cn/phyto.PhytoCell is scalable to different sizes of single-cell datasets,representing a valuable resource for precise identification in cell research. 展开更多
关键词 scRNA-seq PhytoCell Machine learning Ensemble learning Cell subpopulations
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Task-Structured Curriculum Learning for Multi-Task Distillation:Enhancing Step-by-Step Knowledge Transfer in Language Models 认领 引用
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作者 Ahmet Ezgi Aytug Onan 《Computers, Materials & Continua》 SCIE EI 2026年第3期1647-1673,共27页
Knowledge distillation has become a standard technique for compressing large language models into efficient student models,but existing methods often struggle to balance prediction accuracy with explanation quality.Re... Knowledge distillation has become a standard technique for compressing large language models into efficient student models,but existing methods often struggle to balance prediction accuracy with explanation quality.Recent approaches such as Distilling Step-by-Step(DSbS)introduce explanation supervision,yet they apply it in a uniform manner that may not fully exploit the different learning dynamics of prediction and explanation.In this work,we propose a task-structured curriculum learning(TSCL)framework that structures training into three sequential phases:(i)prediction-only,to establish stable feature representations;(ii)joint prediction-explanation,to align task outputs with rationale generation;and(iii)explanation-only,to refine the quality of rationales.This design provides a simple but effective modification to DSbS,requiring no architectural changes and adding negligible training cost.We justify the phase scheduling with ablation studies and convergence analysis,showing that an initial prediction-heavy stage followed by a balanced joint phase improves both stability and explanation alignment.Extensive experiments on five datasets(e-SNLI,ANLI,CommonsenseQA,SVAMP,and MedNLI)demonstrate that TSCL consistently outperforms strong baselines,achieving gains of+1.7-2.6 points in accuracy and 0.8-1.2 in ROUGE-L,corresponding to relative error reductions of up to 21%.Beyond lexical metrics,human evaluation and ERASERstyle faithfulness diagnostics confirm that TSCL produces more faithful and informative explanations.Comparative training curves further reveal faster convergence and lower variance across seeds.Efficiency analysis shows less than 3%overhead in wall-clock training time and no additional inference cost,making the approach practical for realworld deployment.This study demonstrates that a simple task-structured curriculum can significantly improve the effectiveness of knowledge distillation.By separating and sequencing objectives,TSCL achieves a better balance between accuracy,stability,and explanation quality.The framework generalizes across domains,including medical NLI,and offers a principled recipe for future applications in multimodal reasoning and reinforcement learning. 展开更多
关键词 Knowledge distillation curriculum learning language models multi-task learning step-by-step learning
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