With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
Graph Federated Learning(GFL)has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information.However,exi...Graph Federated Learning(GFL)has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information.However,existing GFL approaches often lack the capability for comprehensive feature extraction and adaptive optimization,particularly in non-independent and identically distributed(NON-IID)scenarios where balancing global structural understanding and local node-level detail remains a challenge.To this end,this paper proposes a novel framework called GFL-SAR(Graph Federated Collaborative Learning Framework Based on Structural Amplification and Attention Refinement),which enhances the representation learning capability of graph data through a dual-branch collaborative design.Specifically,we propose the Structural Insight Amplifier(SIA),which utilizes an improved Graph Convolutional Network(GCN)to strengthen structural awareness and improve modeling of topological patterns.In parallel,we propose the Attentive Relational Refiner(ARR),which employs an enhanced Graph Attention Network(GAT)to perform fine-grained modeling of node relationships and neighborhood features,thereby improving the expressiveness of local interactions and preserving critical contextual information.GFL-SAR effectively integrates multi-scale features from every branch via feature fusion and federated optimization,thereby addressing existing GFL limitations in structural modeling and feature representation.Experiments on standard benchmark datasets including Cora,Citeseer,Polblogs,and Cora_ML demonstrate that GFL-SAR achieves superior performance in classification accuracy,convergence speed,and robustness compared to existing methods,confirming its effectiveness and generalizability in GFL tasks.展开更多
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal...Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.展开更多
The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an...The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.展开更多
To protect user privacy and data security,the integration of Federated Learning(FL)and blockchain has become an emerging research hotspot.However,the limited throughput and high communication complexity of traditional...To protect user privacy and data security,the integration of Federated Learning(FL)and blockchain has become an emerging research hotspot.However,the limited throughput and high communication complexity of traditional blockchains limit their application in large-scale FL tasks,and the synchronous traditional FL will also reduce the training efficiency.To address these issues,in this paper,we propose a Directed Acyclic Graph(DAG)blockchain-enabled generalized Federated Dropout(FD)learning strategy,which could improve the efficiency of FL while ensuring the model generalization.Specifically,the DAG maintained by multiple edge servers will guarantee the security and traceability of the data,and the Reputation-based Tips Selection Algorithm(RTSA)is proposed to reduce the blockchain consensus delay.Second,the semi-asynchronous training among Intelligent Devices(IDs)is adopted to improve the training efficiency,and a reputation-based FD technology is proposed to prevent overfitting of the model.In addition,a Hybrid Optimal Resource Allocation(HORA)algorithm is introduced to minimize the network delay.Finally,simulation results demonstrate the effectiveness and superiority of the proposed algorithms.展开更多
Federated Graph Learning (FGL) enables model training without requiring each client to share local graph data, effectively breaking data silos by aggregating the training parameters from each terminal while safeguardi...Federated Graph Learning (FGL) enables model training without requiring each client to share local graph data, effectively breaking data silos by aggregating the training parameters from each terminal while safeguarding data privacy. Traditional FGL relies on a centralized server for model aggregation;however, this central server presents challenges such as a single point of failure and high communication overhead. Additionally, efficiently training a robust personalized local model for each client remains a significant objective in federated graph learning. To address these issues, we propose a decentralized Federated Graph Learning framework with efficient communication, termed Decentralized Federated Graph Learning via Surrogate Model (SD_FGL). In SD_FGL, each client is required to maintain two models: a private model and a surrogate model. The surrogate model is publicly shared and can exchange and update information directly with any client, eliminating the need for a central server and reducing communication overhead. The private model is independently trained by each client, allowing it to calculate similarity with other clients based on local data as well as information shared through the surrogate model. This enables the private model to better adjust its training strategy and selectively update its parameters. Additionally, local differential privacy is incorporated into the surrogate model training process to enhance privacy protection. Testing on three real-world graph datasets demonstrates that the proposed framework improves accuracy while achieving decentralized Federated Graph Learning with lower communication overhead and stronger privacy safeguards.展开更多
Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Num...Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.展开更多
With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices ge...With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.展开更多
In the realm of Intelligent Railway Transportation Systems,effective multi-party collaboration is crucial due to concerns over privacy and data silos.Vertical Federated Learning(VFL)has emerged as a promising approach...In the realm of Intelligent Railway Transportation Systems,effective multi-party collaboration is crucial due to concerns over privacy and data silos.Vertical Federated Learning(VFL)has emerged as a promising approach to facilitate such collaboration,allowing diverse entities to collectively enhance machine learning models without the need to share sensitive training data.However,existing works have highlighted VFL’s susceptibility to privacy inference attacks,where an honest but curious server could potentially reconstruct a client’s raw data from embeddings uploaded by the client.This vulnerability poses a significant threat to VFL-based intelligent railway transportation systems.In this paper,we introduce SensFL,a novel privacy-enhancing method to against privacy inference attacks in VFL.Specifically,SensFL integrates regularization of the sensitivity of embeddings to the original data into the model training process,effectively limiting the information contained in shared embeddings.By reducing the sensitivity of embeddings to the original data,SensFL can effectively resist reverse privacy attacks and prevent the reconstruction of the original data from the embeddings.Extensive experiments were conducted on four distinct datasets and three different models to demonstrate the efficacy of SensFL.Experiment results show that SensFL can effectively mitigate privacy inference attacks while maintaining the accuracy of the primary learning task.These results underscore SensFL’s potential to advance privacy protection technologies within VFL-based intelligent railway systems,addressing critical security concerns in collaborative learning environments.展开更多
As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by...As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.展开更多
Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients a...Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients and the server.However,the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability.Meanwhile,data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks,and standalone personalization tasks may cause severe overfitting issues.To address these limitations,we introduce a federated learning dual optimization model based on clustering and personalization strategy(FedCPS).FedCPS adopts a decentralized approach,where clients identify their cluster membership locally without relying on a centralized clustering algorithm.Building on this,FedCPS introduces personalized training tasks locally,adding a regularization term to control deviations between local and cluster models.This improves the generalization ability of the final model while mitigating overfitting.The use of weight-sharing techniques also reduces the computational cost of central machines.Experimental results on MNIST,FMNIST,CIFAR10,and CIFAR100 datasets demonstrate that our method achieves better personalization effects compared to other personalized federated learning methods,with an average test accuracy improvement of 0.81%–2.96%.Meanwhile,we adjusted the proportion of few-shot clients to evaluate the impact on accuracy across different methods.The experiments show that FedCPS reduces accuracy by only 0.2%–3.7%,compared to 2.1%–10%for existing methods.Our method demonstrates its advantages across diverse data environments.展开更多
Distributed Federated Learning(DFL)technology enables participants to cooperatively train a shared model while preserving the privacy of their local datasets,making it a desirable solution for decentralized and privac...Distributed Federated Learning(DFL)technology enables participants to cooperatively train a shared model while preserving the privacy of their local datasets,making it a desirable solution for decentralized and privacy-preserving Web3 scenarios.However,DFL faces incentive and security challenges in the decentralized framework.To address these issues,this paper presents a Hierarchical Blockchain-enabled DFL(HBDFL)system,which provides a generic solution framework for the DFL-related applications.The proposed system consists of four major components,including a model contribution-based reward mechanism,a Proof of Elapsed Time and Accuracy(PoETA)consensus algorithm,a Distributed Reputation-based Verification Mechanism(DRTM)and an Accuracy-Dependent Throughput Management(ADTM)mechanism.The model contribution-based rewarding mechanism incentivizes network nodes to train models with their local datasets,while the PoETA consensus algorithm optimizes the tradeoff between the shared model accuracy and system throughput.The DRTM improves the system efficiency in consensus,and the ADTM mechanism guarantees that the throughput performance remains within a predefined range while improving the shared model accuracy.The performance of the proposed HBDFL system is evaluated by numerical simulations,with the results showing that the system improves the accuracy of the shared model while maintaining high throughput and ensuring security.展开更多
Network architectures assisted by Generative Artificial Intelligence(GAI)are envisioned as foundational elements of sixth-generation(6G)communication system.To deliver ubiquitous intelligent services and meet diverse ...Network architectures assisted by Generative Artificial Intelligence(GAI)are envisioned as foundational elements of sixth-generation(6G)communication system.To deliver ubiquitous intelligent services and meet diverse service requirements,6G network architecture should offer personalized services to various mobile devices.Federated learning(FL)with personalized local training,as a privacypreserving machine learning(ML)approach,can be applied to address these challenges.In this paper,we propose a meta-learning-based personalized FL(PFL)method that improves both communication and computation efficiency by utilizing over-the-air computations.Its“pretraining-and-fine-tuning”principle makes it particularly suitable for enabling edge nodes to access personalized GAI services while preserving local privacy.Experiment results demonstrate the outperformance and efficacy of the proposed algorithm,and notably indicate enhanced communication efficiency without compromising accuracy.展开更多
In the rapidly evolving landscape of television advertising,optimizing ad schedules to maximize viewer engagement and revenue has become significant.Traditional methods often operate in silos,limiting the potential in...In the rapidly evolving landscape of television advertising,optimizing ad schedules to maximize viewer engagement and revenue has become significant.Traditional methods often operate in silos,limiting the potential insights gained from broader data analysis due to concerns over privacy and data sharing.This article introduces a novel approach that leverages Federated Learning(FL)to enhance TV ad schedule optimization,combining the strengths of local optimization techniques with the power of global Machine Learning(ML)models to uncover actionable insights without compromising data privacy.It combines linear programming for initial ads scheduling optimization with ML—specifically,a K-Nearest Neighbors(KNN)model—for predicting ad spot positions.Taking into account the diversity and the difficulty of the ad-scheduling problem,we propose a prescriptivepredictive approach in which first the position of the ads is optimized(using Google’s OR-Tools CP-SAT)and then the scheduled position of all ads will be the result of the optimization problem.Second,this output becomes the target of a predictive task that predicts the position of new entries based on their characteristics ensuring the implementation of the scheduling at large scale(using KNN,Light Gradient Boosting Machine and Random Forest).Furthermore,we explore the integration of FL to enhance predictive accuracy and strategic insight across different broadcasting networks while preserving data privacy.The FL approach resulted in 8750 ads being precisely matched to their optimal category placements,showcasing an alignment with the intended diversity objectives.Additionally,there was a minimal deviation observed,with 1133 ads positioned within a one-category variance from their ideal placement in the original dataset.展开更多
FedCognis is a secure and scalable federated learning framework designed for continuous anomaly detection in Industrial Internet of Things-enabled Cognitive Cities(IIoTCC).It introduces two key innovations:a Quantum S...FedCognis is a secure and scalable federated learning framework designed for continuous anomaly detection in Industrial Internet of Things-enabled Cognitive Cities(IIoTCC).It introduces two key innovations:a Quantum Secure Authentication(QSA)mechanism for adversarial defense and integrity validation,and a Self-Attention Long Short-Term Memory(SALSTM)model for high-accuracy spatiotemporal anomaly detection.Addressing core challenges in traditional Federated Learning(FL)—such as model poisoning,communication overhead,and concept drift—FedCognis integrates dynamic trust-based aggregation and lightweight cryptographic verification to ensure secure,real-time operation across heterogeneous IIoT domains including utilities,public safety,and traffic systems.Evaluated on the WUSTL-IIoTCC-2021 dataset,FedCognis achieves 94.5%accuracy,0.941 AUC for precision-recall,and 0.896 ROC-AUC,while reducing bandwidth consumption by 72%.The framework demonstrates sublinear computational complexity and a resilience score of 96.56%across six security dimensions.These results confirm FedCognis as a robust and adaptive anomaly detection solution suitable for deployment in large-scale cognitive urban infrastructures.展开更多
With the deep integration of edge computing,5G and Artificial Intelligence ofThings(AIoT)technologies,the large-scale deployment of intelligent terminal devices has given rise to data silos and privacy security challe...With the deep integration of edge computing,5G and Artificial Intelligence ofThings(AIoT)technologies,the large-scale deployment of intelligent terminal devices has given rise to data silos and privacy security challenges in sensing-computing fusion scenarios.Traditional federated learning(FL)algorithms face significant limitations in practical applications due to client drift,model bias,and resource constraints under non-independent and identically distributed(Non-IID)data,as well as the computational overhead and utility loss caused by privacy-preserving techniques.To address these issues,this paper proposes an Efficient and Privacy-enhancing Clustering Federated Learning method(FedEPC).This method introduces a dual-round client selection mechanism to optimize training.First,the Sparsity-based Privacy-preserving Representation Extraction Module(SPRE)and Adaptive Isomorphic Devices Clustering Module(AIDC)cluster clients based on privacy-sensitive features.Second,the Context-aware Incluster Client Selection Module(CICS)dynamically selects representative devices for training,ensuring heterogeneous data distributions are fully represented.By conducting federated training within clusters and aggregating personalized models,FedEPC effectively mitigates weight divergence caused by data heterogeneity,reduces the impact of client drift and straggler issues.Experimental results demonstrate that FedEPC significantly improves test accuracy in highly Non-IID data scenarios compared to FedAvg and existing clustering FL methods.By ensuring privacy security,FedEPC provides an efficient and robust solution for FL in resource-constrained devices within sensing-computing fusion scenarios,offering both theoretical value and engineering practicality.展开更多
Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes...Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).展开更多
Federated learning combines with fog computing to transform data sharing into model sharing,which solves the issues of data isolation and privacy disclosure in fog computing.However,existing studies focus on centraliz...Federated learning combines with fog computing to transform data sharing into model sharing,which solves the issues of data isolation and privacy disclosure in fog computing.However,existing studies focus on centralized single-layer aggregation federated learning architecture,which lack the consideration of cross-domain and asynchronous robustness of federated learning,and rarely integrate verification mechanisms from the perspective of incentives.To address the above challenges,we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL)framework based on dual aggregation for cross-domain scenarios.In particular,we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains.Second,we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models'availability of intra-domain user.Furthermore,we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain.Finally,security analysis demonstrates the security and privacy effectiveness of BSAFL,and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.展开更多
False Data Injection Attack(FDIA),a disruptive cyber threat,is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems,leading to system unr...False Data Injection Attack(FDIA),a disruptive cyber threat,is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems,leading to system unreliability,data integrity loss and operational vulnerability exposure.Given its widespread harm and impact,conducting in-depth research on FDIA detection is vitally important.This paper innovatively introduces a FDIA detection scheme:A Protected Federated Deep Learning(ProFed),which leverages Federated Averaging algorithm(FedAvg)as a foundational framework to fortify data security,harnesses pre-trained enhanced spatial-temporal graph neural networks(STGNN)to perform localized model training and integrates the Cheon-Kim-Kim-Song(CKKS)homomorphic encryption system to secure sensitive information.Simulation tests on IEEE 14-bus and IEEE 118-bus systems demonstrate that our proposed method outperforms other state-of-the-art detection methods across all evaluation metrics,with peak improvements reaching up to 35%.展开更多
The increasing deployment of Internet of Things(IoT)devices has introduced significant security chal-lenges,including identity spoofing,unauthorized access,and data integrity breaches.Traditional security mechanisms r...The increasing deployment of Internet of Things(IoT)devices has introduced significant security chal-lenges,including identity spoofing,unauthorized access,and data integrity breaches.Traditional security mechanisms rely on centralized frameworks that suffer from single points of failure,scalability issues,and inefficiencies in real-time security enforcement.To address these limitations,this study proposes the Blockchain-Enhanced Trust and Access Control for IoT Security(BETAC-IoT)model,which integrates blockchain technology,smart contracts,federated learning,and Merkle tree-based integrity verification to enhance IoT security.The proposed model eliminates reliance on centralized authentication by employing decentralized identity management,ensuring tamper-proof data storage,and automating access control through smart contracts.Experimental evaluation using a synthetic IoT dataset shows that the BETAC-IoT model improves access control enforcement accuracy by 92%,reduces device authentication time by 52%(from 2.5 to 1.2 s),and enhances threat detection efficiency by 7%(from 85%to 92%)using federated learning.Additionally,the hybrid blockchain architecture achieves a 300%increase in transaction throughput when comparing private blockchain performance(1200 TPS)to public chains(300 TPS).Access control enforcement accuracy was quantified through confusion matrix analysis,with high precision and minimal false positives observed across access decision categories.Although the model presents advantages in security and scalability,challenges such as computational overhead,blockchain storage constraints,and interoperability with existing IoT systems remain areas for future research.This study contributes to advancing decentralized security frameworks for IoT,providing a resilient and scalable solution for securing connected environments.展开更多
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
基金supported by National Natural Science Foundation of China(62466045)Inner Mongolia Natural Science Foundation Project(2021LHMS06003)Inner Mongolia University Basic Research Business Fee Project(114).
文摘Graph Federated Learning(GFL)has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information.However,existing GFL approaches often lack the capability for comprehensive feature extraction and adaptive optimization,particularly in non-independent and identically distributed(NON-IID)scenarios where balancing global structural understanding and local node-level detail remains a challenge.To this end,this paper proposes a novel framework called GFL-SAR(Graph Federated Collaborative Learning Framework Based on Structural Amplification and Attention Refinement),which enhances the representation learning capability of graph data through a dual-branch collaborative design.Specifically,we propose the Structural Insight Amplifier(SIA),which utilizes an improved Graph Convolutional Network(GCN)to strengthen structural awareness and improve modeling of topological patterns.In parallel,we propose the Attentive Relational Refiner(ARR),which employs an enhanced Graph Attention Network(GAT)to perform fine-grained modeling of node relationships and neighborhood features,thereby improving the expressiveness of local interactions and preserving critical contextual information.GFL-SAR effectively integrates multi-scale features from every branch via feature fusion and federated optimization,thereby addressing existing GFL limitations in structural modeling and feature representation.Experiments on standard benchmark datasets including Cora,Citeseer,Polblogs,and Cora_ML demonstrate that GFL-SAR achieves superior performance in classification accuracy,convergence speed,and robustness compared to existing methods,confirming its effectiveness and generalizability in GFL tasks.
文摘Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.
基金supported in part by the National Key R&D Program of China under Grant 2021YFB1714100in part by the National Natural Science Foundation of China(NSFC)under Grant 62371082 and 62001076in part by the Natural Science Foundation of Chongqing under Grant CSTB2023NSCQ-MSX0726 and cstc2020jcyjmsxmX0878.
文摘To protect user privacy and data security,the integration of Federated Learning(FL)and blockchain has become an emerging research hotspot.However,the limited throughput and high communication complexity of traditional blockchains limit their application in large-scale FL tasks,and the synchronous traditional FL will also reduce the training efficiency.To address these issues,in this paper,we propose a Directed Acyclic Graph(DAG)blockchain-enabled generalized Federated Dropout(FD)learning strategy,which could improve the efficiency of FL while ensuring the model generalization.Specifically,the DAG maintained by multiple edge servers will guarantee the security and traceability of the data,and the Reputation-based Tips Selection Algorithm(RTSA)is proposed to reduce the blockchain consensus delay.Second,the semi-asynchronous training among Intelligent Devices(IDs)is adopted to improve the training efficiency,and a reputation-based FD technology is proposed to prevent overfitting of the model.In addition,a Hybrid Optimal Resource Allocation(HORA)algorithm is introduced to minimize the network delay.Finally,simulation results demonstrate the effectiveness and superiority of the proposed algorithms.
基金supported by InnerMongolia Natural Science Foundation Project(2021LHMS06003)Inner Mongolia University Basic Research Business Fee Project(114).
文摘Federated Graph Learning (FGL) enables model training without requiring each client to share local graph data, effectively breaking data silos by aggregating the training parameters from each terminal while safeguarding data privacy. Traditional FGL relies on a centralized server for model aggregation;however, this central server presents challenges such as a single point of failure and high communication overhead. Additionally, efficiently training a robust personalized local model for each client remains a significant objective in federated graph learning. To address these issues, we propose a decentralized Federated Graph Learning framework with efficient communication, termed Decentralized Federated Graph Learning via Surrogate Model (SD_FGL). In SD_FGL, each client is required to maintain two models: a private model and a surrogate model. The surrogate model is publicly shared and can exchange and update information directly with any client, eliminating the need for a central server and reducing communication overhead. The private model is independently trained by each client, allowing it to calculate similarity with other clients based on local data as well as information shared through the surrogate model. This enables the private model to better adjust its training strategy and selectively update its parameters. Additionally, local differential privacy is incorporated into the surrogate model training process to enhance privacy protection. Testing on three real-world graph datasets demonstrates that the proposed framework improves accuracy while achieving decentralized Federated Graph Learning with lower communication overhead and stronger privacy safeguards.
文摘Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.
基金supported by the Shandong Province Science and Technology Project(2023TSGC0509,2022TSGC2234)Qingdao Science and Technology Plan Project(23-1-5-yqpy-2-qy)Open Topic Grants of Anhui Province Key Laboratory of Intelligent Building&Building Energy Saving,Anhui Jianzhu University(IBES2024KF08).
文摘With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.
基金supported by Systematic Major Project of Shuohuang Railway Development Co.,Ltd.,National Energy Group(Grant Number:SHTL-23-31)Beijing Natural Science Foundation(U22B2027).
文摘In the realm of Intelligent Railway Transportation Systems,effective multi-party collaboration is crucial due to concerns over privacy and data silos.Vertical Federated Learning(VFL)has emerged as a promising approach to facilitate such collaboration,allowing diverse entities to collectively enhance machine learning models without the need to share sensitive training data.However,existing works have highlighted VFL’s susceptibility to privacy inference attacks,where an honest but curious server could potentially reconstruct a client’s raw data from embeddings uploaded by the client.This vulnerability poses a significant threat to VFL-based intelligent railway transportation systems.In this paper,we introduce SensFL,a novel privacy-enhancing method to against privacy inference attacks in VFL.Specifically,SensFL integrates regularization of the sensitivity of embeddings to the original data into the model training process,effectively limiting the information contained in shared embeddings.By reducing the sensitivity of embeddings to the original data,SensFL can effectively resist reverse privacy attacks and prevent the reconstruction of the original data from the embeddings.Extensive experiments were conducted on four distinct datasets and three different models to demonstrate the efficacy of SensFL.Experiment results show that SensFL can effectively mitigate privacy inference attacks while maintaining the accuracy of the primary learning task.These results underscore SensFL’s potential to advance privacy protection technologies within VFL-based intelligent railway systems,addressing critical security concerns in collaborative learning environments.
文摘As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.
基金supported by the Foundation of President of Hebei University(XZJJ202303).
文摘Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients and the server.However,the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability.Meanwhile,data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks,and standalone personalization tasks may cause severe overfitting issues.To address these limitations,we introduce a federated learning dual optimization model based on clustering and personalization strategy(FedCPS).FedCPS adopts a decentralized approach,where clients identify their cluster membership locally without relying on a centralized clustering algorithm.Building on this,FedCPS introduces personalized training tasks locally,adding a regularization term to control deviations between local and cluster models.This improves the generalization ability of the final model while mitigating overfitting.The use of weight-sharing techniques also reduces the computational cost of central machines.Experimental results on MNIST,FMNIST,CIFAR10,and CIFAR100 datasets demonstrate that our method achieves better personalization effects compared to other personalized federated learning methods,with an average test accuracy improvement of 0.81%–2.96%.Meanwhile,we adjusted the proportion of few-shot clients to evaluate the impact on accuracy across different methods.The experiments show that FedCPS reduces accuracy by only 0.2%–3.7%,compared to 2.1%–10%for existing methods.Our method demonstrates its advantages across diverse data environments.
文摘Distributed Federated Learning(DFL)technology enables participants to cooperatively train a shared model while preserving the privacy of their local datasets,making it a desirable solution for decentralized and privacy-preserving Web3 scenarios.However,DFL faces incentive and security challenges in the decentralized framework.To address these issues,this paper presents a Hierarchical Blockchain-enabled DFL(HBDFL)system,which provides a generic solution framework for the DFL-related applications.The proposed system consists of four major components,including a model contribution-based reward mechanism,a Proof of Elapsed Time and Accuracy(PoETA)consensus algorithm,a Distributed Reputation-based Verification Mechanism(DRTM)and an Accuracy-Dependent Throughput Management(ADTM)mechanism.The model contribution-based rewarding mechanism incentivizes network nodes to train models with their local datasets,while the PoETA consensus algorithm optimizes the tradeoff between the shared model accuracy and system throughput.The DRTM improves the system efficiency in consensus,and the ADTM mechanism guarantees that the throughput performance remains within a predefined range while improving the shared model accuracy.The performance of the proposed HBDFL system is evaluated by numerical simulations,with the results showing that the system improves the accuracy of the shared model while maintaining high throughput and ensuring security.
基金supported in part by the National Key R&D Program of China under Grant 2024YFE0200700in part by the National Natural Science Foundation of China under Grant 62201504.
文摘Network architectures assisted by Generative Artificial Intelligence(GAI)are envisioned as foundational elements of sixth-generation(6G)communication system.To deliver ubiquitous intelligent services and meet diverse service requirements,6G network architecture should offer personalized services to various mobile devices.Federated learning(FL)with personalized local training,as a privacypreserving machine learning(ML)approach,can be applied to address these challenges.In this paper,we propose a meta-learning-based personalized FL(PFL)method that improves both communication and computation efficiency by utilizing over-the-air computations.Its“pretraining-and-fine-tuning”principle makes it particularly suitable for enabling edge nodes to access personalized GAI services while preserving local privacy.Experiment results demonstrate the outperformance and efficacy of the proposed algorithm,and notably indicate enhanced communication efficiency without compromising accuracy.
基金supported by a grant of the Ministry of Research,Innovation and Digitization,CNCS/CCCDI-UEFISCDI,project number COFUND-DUT-OPEN4CEC-1,within PNCDI IV.
文摘In the rapidly evolving landscape of television advertising,optimizing ad schedules to maximize viewer engagement and revenue has become significant.Traditional methods often operate in silos,limiting the potential insights gained from broader data analysis due to concerns over privacy and data sharing.This article introduces a novel approach that leverages Federated Learning(FL)to enhance TV ad schedule optimization,combining the strengths of local optimization techniques with the power of global Machine Learning(ML)models to uncover actionable insights without compromising data privacy.It combines linear programming for initial ads scheduling optimization with ML—specifically,a K-Nearest Neighbors(KNN)model—for predicting ad spot positions.Taking into account the diversity and the difficulty of the ad-scheduling problem,we propose a prescriptivepredictive approach in which first the position of the ads is optimized(using Google’s OR-Tools CP-SAT)and then the scheduled position of all ads will be the result of the optimization problem.Second,this output becomes the target of a predictive task that predicts the position of new entries based on their characteristics ensuring the implementation of the scheduling at large scale(using KNN,Light Gradient Boosting Machine and Random Forest).Furthermore,we explore the integration of FL to enhance predictive accuracy and strategic insight across different broadcasting networks while preserving data privacy.The FL approach resulted in 8750 ads being precisely matched to their optimal category placements,showcasing an alignment with the intended diversity objectives.Additionally,there was a minimal deviation observed,with 1133 ads positioned within a one-category variance from their ideal placement in the original dataset.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘FedCognis is a secure and scalable federated learning framework designed for continuous anomaly detection in Industrial Internet of Things-enabled Cognitive Cities(IIoTCC).It introduces two key innovations:a Quantum Secure Authentication(QSA)mechanism for adversarial defense and integrity validation,and a Self-Attention Long Short-Term Memory(SALSTM)model for high-accuracy spatiotemporal anomaly detection.Addressing core challenges in traditional Federated Learning(FL)—such as model poisoning,communication overhead,and concept drift—FedCognis integrates dynamic trust-based aggregation and lightweight cryptographic verification to ensure secure,real-time operation across heterogeneous IIoT domains including utilities,public safety,and traffic systems.Evaluated on the WUSTL-IIoTCC-2021 dataset,FedCognis achieves 94.5%accuracy,0.941 AUC for precision-recall,and 0.896 ROC-AUC,while reducing bandwidth consumption by 72%.The framework demonstrates sublinear computational complexity and a resilience score of 96.56%across six security dimensions.These results confirm FedCognis as a robust and adaptive anomaly detection solution suitable for deployment in large-scale cognitive urban infrastructures.
基金funded by the State Grid Corporation Science and Technology Project“Research and Application of Key Technologies for Integrated Sensing and Computing for Intelligent Operation of Power Grid”(Grant No.5700-202318596A-3-2-ZN).
文摘With the deep integration of edge computing,5G and Artificial Intelligence ofThings(AIoT)technologies,the large-scale deployment of intelligent terminal devices has given rise to data silos and privacy security challenges in sensing-computing fusion scenarios.Traditional federated learning(FL)algorithms face significant limitations in practical applications due to client drift,model bias,and resource constraints under non-independent and identically distributed(Non-IID)data,as well as the computational overhead and utility loss caused by privacy-preserving techniques.To address these issues,this paper proposes an Efficient and Privacy-enhancing Clustering Federated Learning method(FedEPC).This method introduces a dual-round client selection mechanism to optimize training.First,the Sparsity-based Privacy-preserving Representation Extraction Module(SPRE)and Adaptive Isomorphic Devices Clustering Module(AIDC)cluster clients based on privacy-sensitive features.Second,the Context-aware Incluster Client Selection Module(CICS)dynamically selects representative devices for training,ensuring heterogeneous data distributions are fully represented.By conducting federated training within clusters and aggregating personalized models,FedEPC effectively mitigates weight divergence caused by data heterogeneity,reduces the impact of client drift and straggler issues.Experimental results demonstrate that FedEPC significantly improves test accuracy in highly Non-IID data scenarios compared to FedAvg and existing clustering FL methods.By ensuring privacy security,FedEPC provides an efficient and robust solution for FL in resource-constrained devices within sensing-computing fusion scenarios,offering both theoretical value and engineering practicality.
基金funded by the Northern Border University,Arar,KSA,under the project number“NBU-FFR-2025-3555-07”.
文摘Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).
基金supported in part by the National Key Research and Development Program of China under Grant No.2021YFB3101100in part by the National Natural Science Foundation of China under Grant 62272123,62272102,62272124+2 种基金in part by the Project of High-level Innovative Talents of Guizhou Province under Grant[2020]6008in part by the Science and Technology Program of Guizhou Province under Grant No.[2020]5017,No.[2022]065in part by the Guangxi Key Laboratory of Cryptography and Information Security under Grant GCIS202105。
文摘Federated learning combines with fog computing to transform data sharing into model sharing,which solves the issues of data isolation and privacy disclosure in fog computing.However,existing studies focus on centralized single-layer aggregation federated learning architecture,which lack the consideration of cross-domain and asynchronous robustness of federated learning,and rarely integrate verification mechanisms from the perspective of incentives.To address the above challenges,we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL)framework based on dual aggregation for cross-domain scenarios.In particular,we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains.Second,we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models'availability of intra-domain user.Furthermore,we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain.Finally,security analysis demonstrates the security and privacy effectiveness of BSAFL,and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.
基金supported in part by the Sichuan Science and Technology Program(2024YFHZ0015)the Key Laboratory of Data Protection and Intelligent Management,Ministry of Education,Sichuan University(SCUSACXYD202401).
文摘False Data Injection Attack(FDIA),a disruptive cyber threat,is becoming increasingly detrimental to smart grids with the deepening integration of information technology and physical power systems,leading to system unreliability,data integrity loss and operational vulnerability exposure.Given its widespread harm and impact,conducting in-depth research on FDIA detection is vitally important.This paper innovatively introduces a FDIA detection scheme:A Protected Federated Deep Learning(ProFed),which leverages Federated Averaging algorithm(FedAvg)as a foundational framework to fortify data security,harnesses pre-trained enhanced spatial-temporal graph neural networks(STGNN)to perform localized model training and integrates the Cheon-Kim-Kim-Song(CKKS)homomorphic encryption system to secure sensitive information.Simulation tests on IEEE 14-bus and IEEE 118-bus systems demonstrate that our proposed method outperforms other state-of-the-art detection methods across all evaluation metrics,with peak improvements reaching up to 35%.
文摘The increasing deployment of Internet of Things(IoT)devices has introduced significant security chal-lenges,including identity spoofing,unauthorized access,and data integrity breaches.Traditional security mechanisms rely on centralized frameworks that suffer from single points of failure,scalability issues,and inefficiencies in real-time security enforcement.To address these limitations,this study proposes the Blockchain-Enhanced Trust and Access Control for IoT Security(BETAC-IoT)model,which integrates blockchain technology,smart contracts,federated learning,and Merkle tree-based integrity verification to enhance IoT security.The proposed model eliminates reliance on centralized authentication by employing decentralized identity management,ensuring tamper-proof data storage,and automating access control through smart contracts.Experimental evaluation using a synthetic IoT dataset shows that the BETAC-IoT model improves access control enforcement accuracy by 92%,reduces device authentication time by 52%(from 2.5 to 1.2 s),and enhances threat detection efficiency by 7%(from 85%to 92%)using federated learning.Additionally,the hybrid blockchain architecture achieves a 300%increase in transaction throughput when comparing private blockchain performance(1200 TPS)to public chains(300 TPS).Access control enforcement accuracy was quantified through confusion matrix analysis,with high precision and minimal false positives observed across access decision categories.Although the model presents advantages in security and scalability,challenges such as computational overhead,blockchain storage constraints,and interoperability with existing IoT systems remain areas for future research.This study contributes to advancing decentralized security frameworks for IoT,providing a resilient and scalable solution for securing connected environments.
