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A Survey of Federated Learning:Advances in Architecture,Synchronization,and Security Threats 认领 引用 被引量：1
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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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Trustworthy Personalized Federated Recommender System with Blockchain-Assisted Decentralized Reward Management 认领 引用
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作者 Waqar Ali May Altulyan +2 位作者 Ghulam Farooque Siyuan Li Jie Shao 《Computers, Materials & Continua》 SCIE EI 2026年第6期1919-1933,共15页
Federated recommender systems(FedRS)enable collaborative model training while preserving user privacy,yet they remain vulnerable to adversarial attacks,unreliable client updates,and misaligned incentives in decentrali... Federated recommender systems(FedRS)enable collaborative model training while preserving user privacy,yet they remain vulnerable to adversarial attacks,unreliable client updates,and misaligned incentives in decentralized environments.Existing approaches struggle to jointly preserve personalization,robustness,and trust when user data are highly non-IID and recommendation quality is governed by ranking-oriented objectives.To address these challenges,we propose a Trustworthy Federated Recommender System(T-FedRS)that extends federated neural collaborative filtering by integrating a ranking-aware reputation mechanism and a lightweight blockchain layer for transparent incentive allocation.Personalization is preserved through locally maintained user embeddings,while item parameters are aggregated using reputation-weighted fusion to suppress malicious updates.Decentralized reputation scores are derived from top-K ranking metrics evaluated on a public probe set and recorded on-chain,enabling verifiable and tamper-resistant reward settlement without exposing raw data or full model updates.Extensive experiments on multiple benchmark datasets demonstrate that T-FedRS consistently outperforms state-of-the-art federated recommender baselines in recommendation accuracy,maintains stable convergence under non-IID data,and remains robust against a high fraction of shilling and model poisoning clients.These findings establish T-FedRS as a practical and effective solution for building trustworthy and personalized FedRS in decentralized settings. 展开更多
关键词 Federated recommender systems trustworthy federated learning neural collaborative filtering
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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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Personalized federated learning for semantic communication with collaborative fine-tuning 认领 引用
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作者 Maochuan Wu Juan Li +2 位作者 Jing Xu Bing Chen Kun Zhu 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期306-318,共13页
Semantic Communication(SemCom)is a promising paradigm for future 6G networks,where communication performance hinges on the effectiveness of SemCom models,particularly the source-channel encoder and decoder.However,tra... Semantic Communication(SemCom)is a promising paradigm for future 6G networks,where communication performance hinges on the effectiveness of SemCom models,particularly the source-channel encoder and decoder.However,training these models faces significant challenges.Firstly,the privacy-sensitive nature of communication data discourages users from uploading data to centralized servers.Secondly,heterogeneous local data distributions and diverse communication counterparts of different users necessitate personalized SemCom models.Specifically,a user's encoder must align with its receivers'decoders and the transmitted data distribution,while its decoder must adapt to the user's transmitters and received data distribution.To address these challenges,we propose FineFed,a personalized federated learning method with collaborative fine-tuning.Initially,a unified global model is trained distributively via federated learning,eliminating data uploads.Subsequently,users iteratively fine-tune encoders and decoders collaboratively,achieving SemCom model personalization.For encoder fine-tuning,decoders are fixed and shared with transmitters to address distributed loss calculation issues.Each encoder is fine-tuned using the idea of multi-task learning,treating communication with each receiver as a separate task.Then,encoders are fixed.A user shares its decoder with its own transmitters.These transmitters collaboratively fine-tune the user's decoder by the idea of federated multitask learning.Experimental results demonstrate that FineFed improves the average performance of federated SemCom models by 1%-7%,bringing it closer to the performance of centrally-trained models. 展开更多
关键词 Semantic communication Federated learning Fine-tuning Personalized federated learning
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Federated Deep Learning in Intelligent Urban Ecosystems:A Systematic Review of Advancements and Applications in Smart Cities,Homes,Buildings,and Healthcare Systems 认领 引用
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作者 Muhammad Adnan Tariq Sunawar Khan +5 位作者 Tehseen Mazhar Tariq Shahzad Sahar Arooj Khmaies Ouahada Muhammad Adnan Khan Habib Hamam 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期218-267,共50页
The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigm... The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigms have fundamental issues in data privacy,regulatory compliance,and ownership silo alongside the scaled limitations of the real-life application.The concept of Federated Deep Learning(FDL)is a privacy-by-design method that will enable the distributed training of machine learning models among distributed clients without sharing raw data and is suitable in heterogeneous urban settings.It is an overview of the privacy-preserving developments in FDL as of 2018-2025 with a narrow scope on its usage in smart cities(traffic prediction,environmental monitoring,energy grids),smart homes/buildings/IoT(non-intrusive load monitoring,HVAC optimization,anomaly detection)and the healthcare application(medical imaging,Electronic Health Records(EHR)analysis,remote monitoring).It gives coherent taxonomy,domain pipelines,comparative analyses of privacy mechanisms(differential privacy,secure aggregation,Homomorphic Encryption(HE),Trusted Execution Environments(TEEs),blockchain enhanced and hybrids),system structures,securityobustness defense,deployment/Machine Learning Operation(MLOps)issues,and the longstanding challenges(non-IID heterogeneity,communication efficiency,fairness,and sustainability).Some of the contributions made are structured comparisons of privacy threats,practical design advice on urban areas,recognition of open problems,and a research roadmap into the future up to 2035.The paper brings out the transformational worth of FDL in building credible,scalable,and sustainable intelligent urban ecosystems and the need to do further interdisciplinary research in standardization,real-world testbeds,and ethical governance. 展开更多
关键词 Federated deep learning(FDL) privacy-preserving AI smart cities smart homes/buildings federated healthcare intelligent urban ecosystems IoT
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When Federated Learning Meets Large Language Models:Taxonomy,Challenges,and Opportunities 认领 引用
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作者 Shan Jiang Wenxin You +2 位作者 Haoran Zhang Shichang Xuan Jiaxing Shen 《Computers, Materials & Continua》 SCIE EI 2026年第8期1-34,共34页
Large Language Models(LLMs)have been playing a transformative role in natural language understanding and generation,yet adapting LLMs to domain-specific and privacy-sensitive data remains challenging under centralized... Large Language Models(LLMs)have been playing a transformative role in natural language understanding and generation,yet adapting LLMs to domain-specific and privacy-sensitive data remains challenging under centralized training.Federated Learning(FL)provides a promising alternative by enabling training LLMs collaboratively without sharing raw data.However,integrating FL and LLMs introduces new challenges,including model size,device heterogeneity,non-IID data,and alignment requirements.This survey offers a structured overview of the federated LLM ecosystem.We present a comprehensive taxonomy encompassing system architectures,advanced data strategies for addressing heterogeneity,and retrieval-augmented generation in federated contexts.Additionally,we review efficient adaptation methods that enable LLM tuning on resource-constrained clients and analyze data security and privacy concerns.We conclude by summarizing emerging applications in healthcare,industry,software engineering,and finance,and by outlining open problems and research opportunities for scalable,secure,and responsible federated LLM deployment. 展开更多
关键词 Large language models federated learning foundation models federated large language models
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An Adaptive Federated Learning with XGBoost Ensembles for Intrusion Detection in Heterogeneous IoT Networks 认领 引用
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作者 Abdulaziz A.Alsulami Qasem Abu Al-Haija +4 位作者 Rayed Alakhtar Ahmad J.Tayeb Badraddin Alturki Huda Alsobhi Rayan A.Alsemmeari 《Computers, Materials & Continua》 SCIE EI 2026年第9期1896-1927,共32页
The rapid growth of the Internet of Things(IoT)devices has increased the attack area of modern networks,which makes effective intrusion detection systems(IDSs)essential to detect attacks that target IoT infrastructure... The rapid growth of the Internet of Things(IoT)devices has increased the attack area of modern networks,which makes effective intrusion detection systems(IDSs)essential to detect attacks that target IoT infrastructures.Federated learning is a promising approach for collaborative model training in the absence of centralized raw data.Conventional federated approaches rely on fixed client participation and static training configurations,which ensure symmetric treatment of clients despite heterogeneous local data distributions.This can limit convergence and degrade detection performance in non-IID conditions.This paper proposes an Adaptive Action-Based Federated Learning(AAFL)framework for decentralized intrusion detection in heterogeneous IoT environments.The framework dynamically adjusts both participating clients and local training workload at each communication round using a Linear Upper Confidence Bound(LinUCB)contextual bandit controller.The proposed Adaptive-FL model is based on XGBoost boosters and uses quality-weighted server-side ensemble aggregation.At the same time,adaptation is guided by a multi-objective reward that balances classification performance,training latency,communication overhead,and computational cost.The framework is evaluated on CIC IoMT 2024 and RT-IoT2022 under realistic non-IID conditions using stratified 5-fold cross-validation and benchmarked against Static-FL,FedAvg-FL,and a centralized XGBoost upper bound.Experimental results demonstrate that Adaptive-FL outperforms all federated baselines across both datasets,achieving Macro-F1 scores of 98.27%on RT-IoT2022 and 94.21%on CIC IoMT 2024,with statistically significant improvements over Static-FL on both datasets.Adaptive-FL maintains superior classification stability while avoiding raw-data centralization.It remains within 0.67 and 0.35 percentage points of the centralized upper bounds on RT-IoT2022 and CIC IoMT 2024,respectively. 展开更多
关键词 Federated learning Internet of Things(IoT) intrusion detection systems(IDS) XGBoost adaptive federated learning
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Subnetwork-based federated few-shot semantic segmentation of organ images 认领 引用
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作者 Junpeng WU Meng ZHAO Huanping ZHANG 《Optoelectronics Letters》 EI 2026年第5期275-281,共7页
Federated learning(FL),as a distributed learning paradigm,allows multiple medical institutions to collaborate on learning without the need to centralize all client data.However,existing methods pay little attention to... Federated learning(FL),as a distributed learning paradigm,allows multiple medical institutions to collaborate on learning without the need to centralize all client data.However,existing methods pay little attention to more challenging medical image semantic segmentation tasks,especially in the scenario of the imbalanced dataset in federated few-shot learning(FSL).In this paper,we propose a subnetwork-based federated few-shot organ image segmentation method.Firstly,individual clients train using local training samples and then upload local model gradients to the server.The server utilizes their respective local model gradients to update the subnetwork maintained on the server and generate aggregation weights for forming personalized model parameters.Through this method,we can learn the similarities between different clients to address data heterogeneity issues.In addition,to enhance the communication efficiency between clients and the server,we have also designed a personalized layer aggregation strategy,which only transmits partial layer model parameters during the communication process to improve communication efficiency.Finally,we conducted experiments on abdomen magnetic resonance imaging(ABD-MRI)and abdomen computed tomography(ABD-CT)datasets to demonstrate the effectiveness of our method. 展开更多
关键词 subnetwork based federated learning semantic segmentation few shot learning imbalanced dataset medical image semantic segmentation local training federated learning fl collaborate learning
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Personalized and sustainable federated learning in integrated Internet of Things 认领 引用
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作者 Xu Zheng Yifu Zheng +3 位作者 Tingqi Wang Chong Mu Ke Yan Ling Tian 《Digital Communications and Networks》 SCIE EI CSCD 2026年第3期441-450,共10页
Integrated Internet of Things(IoT)brings novel opportunities for pervasive smart services,as these systems al-low for seamless information and resource sharing among IoT devices.Meanwhile,Federated Learning(FL)has eme... Integrated Internet of Things(IoT)brings novel opportunities for pervasive smart services,as these systems al-low for seamless information and resource sharing among IoT devices.Meanwhile,Federated Learning(FL)has emerged as a new framework for distributed deployment of machine learning models and a promising approach for implementing intelligent IoT systems.However,integrated IoT systems are usually composed of diverse IoT devices from different systems,and thus their ownership,roles,data distribution,and capabilities are heteroge-neous.Current FL algorithms mainly focus on handling non-Independent and Identically Distributed(Non-IID)issues,but often result in reduced and unsustainable performance in integrated IoT systems.Therefore,we inves-tigate in this paper the problem of personalized and sustainable FL in integrated IoT systems.First,we argue that different parties in integrated IoT are heterogeneous and limited in available resources for FL,and these parties are also selfish and expect rational outcomes during cooperation,which is essential for guaranteeing the sustain-ability of integrated IoT.Then,this paper provides a novel framework for device selection in FL.It first sets one instance of the model for each device,and iteratively selects devices to participate in model training based on the joint consideration of local model accuracy,similarity of parameters,and remaining resources per device.The proposed method guarantees the rational allocation of resources to maintain balanced model performance across all devices.In this way,the sustainability of the whole IoT system is improved such that no devices will suffer extreme resource exhaustion or poor performance.Finally,extensive evaluation is conducted to validate the advanced performance of the proposed method in integrated IoT systems. 展开更多
关键词 Federated learning Integrated IoT Fairness Sustainability
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Flexible Federated Learning in Machinery Fault Diagnostics With Light Communication 认领 引用
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作者 Xiang Li Weipeng Fan +2 位作者 Shaojie Yang Wei Zhang Xu Li 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期680-691,共12页
While data-driven fault diagnosis methods have been successfully developed in the past years, large amounts of highquality condition monitoring data are generally required to ensure model performance. Due to the high ... While data-driven fault diagnosis methods have been successfully developed in the past years, large amounts of highquality condition monitoring data are generally required to ensure model performance. Due to the high economic and labor costs in data collection, it is difficult for a single user to build an effective database, and exploring data of multiple users for better training becomes a promising solution. However, data privacy is of great importance in the real industries due to conflicts of interest, and direct data aggregation from different users is hardly feasible. To address this issue, a flexible federated learning method is proposed in this paper. Different from most existing methods with identical models under the federation, different customized individual deep neural network models can be used at different clients. Public data are exploited for knowledge transfer. Only the scores on public data are communicated between clients and server, rather than the whole model parameters. That significantly reduces the communication and computational burden.Experiments are carried out on two real-world machinery fault diagnosis datasets, and the results show the proposed method is promising for data privacy-preserving federated learning with flexible models and light communications. 展开更多
关键词 Collaborative modeling data privacy deep learning fault diagnosis federated learning
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PowerVLM:基于Federated Learning与模型剪枝的电力视觉语言大模型 认领 引用
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作者 欧阳旭东 雒鹏鑫 +3 位作者 何绍洋 崔艺林 张中超 闫云凤 《全球能源互联网》 EI CSCD 北大核心 2026年第1期101-111,共11页
智能电网的快速发展衍生出多模态、多源异构的海量电力数据,给人工智能模型在复杂电力场景感知带来了挑战,同时行业数据的敏感性和隐私保护需求进一步限制了通用模型在电力领域的跨场景迁移能力。对此,提出了一种基于Federated Learnin... 智能电网的快速发展衍生出多模态、多源异构的海量电力数据,给人工智能模型在复杂电力场景感知带来了挑战,同时行业数据的敏感性和隐私保护需求进一步限制了通用模型在电力领域的跨场景迁移能力。对此,提出了一种基于Federated Learning与模型剪枝的电力视觉语言大模型。提出了一种基于类别引导的电力视觉语言大模型PowerVLM,设计了类别引导增强模块,增强模型对电力图文数据的理解和问答能力;采用FL的强化学习训练策略,在满足数据隐私保护下,降低域间差异对模型性能的影响;最后,提出了一种基于信息决议的模型剪枝算法,可实现低训练参数的模型高效微调。分别在变电巡检、输电任务、作业安监3种典型电力场景开展实验,结果表明,该方法在电力场景多模态问答任务中的METEOR、BLEU和CIDEr等各项指标均表现优异,为电力场景智能感知提供了新的技术思路和方法支撑。 展开更多
关键词 智能电网 人工智能 视觉语言大模型 Federated Learning 模型剪枝
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Risk-Aware Adaptive Federated Learning for Cyber-Secure Edge-AI in Smart Edge-IoT Environments 认领 引用
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作者 Tanveer Ahmad Tahani Alsubait +2 位作者 Amina Salhi Amani Ibraheem Muhammad Asim Saleem 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期1439-1461,共23页
The rapid adoption of Edge-AI in smart edge-IoT environments has dramatically led to an augmented vulnerability to cyber risks arising from distributed learning,data heterogeneity,and adversarial manipulation.This pap... The rapid adoption of Edge-AI in smart edge-IoT environments has dramatically led to an augmented vulnerability to cyber risks arising from distributed learning,data heterogeneity,and adversarial manipulation.This paper proposes a new risk-aware adaptive learningmodel that federated Edge-AI systems explicitly simulates cyber risk in the process of local training and global aggregation.The proposed solution combines stochastic optimization and adversarial risk bounding with adaptive gradient correction to develop strong learning in non-IID data distributions and malicious client behavior.Convergence guarantees are defined by the theoretical analysis in the case of limited adversarial perturbations.The proposed framework achieves up to 95%detection accuracy and demonstrates more than 20%improvement in robustness,where robustness is defined as the relative degradation in detection performance under adversarial perturbations.The performance is evaluated against state-of-the-art baselines,including HADA-FL and centralized training on the Edge-IIoTset dataset,with results reported as averages over multiple randomized runs.Furthermore,the model converges within 50 communication rounds,which corresponds to a fixed training horizon rather than an early-stopping criterion.These findings demonstrate the usefulness of risk-sensitive adaptive learning in safe and trustworthy Edge-AI implementation in a new generation edge-IoT environment. 展开更多
关键词 Edge AI federated learning(FL) adaptive learning adversarial robustness distributed optimization
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Hybrid Ensemble and Federated Learning Framework for Privacy-Preserving Cardiovascular MRI Segmentation 认领 引用
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作者 Karim Gasmi Afrah Alanazi +4 位作者 Inam Alanazi Sahar Almenwer Norah Alanazi Sarah Almaghrabi Samia Yahyaoui 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1245-1287,共43页
Cardiac magnetic resonance imaging(MRI)segmentation is an essential aspect of quantitative cardiovascular analysis,facilitating accurate evaluation of ventricular volumes,myocardial mass,and functional parameters.Deep... Cardiac magnetic resonance imaging(MRI)segmentation is an essential aspect of quantitative cardiovascular analysis,facilitating accurate evaluation of ventricular volumes,myocardial mass,and functional parameters.Deep learning-based segmentation models have shown strong performance on benchmark datasets such as ACDC,but they remain challenging to deploy in real-world multi-centre settings.Data privacy laws make it hard to share data across institutions,and differences in imaging protocols and patient populationsmean that data is not always distributed in the same way(non-IID).This can have a big impact on how wellmodels work together and how well they generalise.To address these issues,we first evaluate advanced segmentation architectures,including UNet++and FPN with EfficientNet-based encoders,and assess multiple hybrid combinations at the probability level.We further improve the ensemble strategy by using a genetic algorithm to automatically identify the optimal model-weighting scheme,rather than fixed combination coefficients.The genetic algorithm explores the solution space to identify the optimal weight configuration based on segmentation metrics.The best hybrid configuration is then chosen as the input architecture for the federated learning stage.We propose a privacy-preserving federated ensemble framework that enables multiple clients to collaboratively train segmentation models without sharing raw MRI data.We methodically evaluate three federated optimisation strategies:FedAvg under IID and non-IID client distributions,and FedProx,which incorporates proximal regularisation to reduce client drift.The genetically optimised ensemble is always used in all federated setups.A thorough analysis of ACDC testing volumes employing overlap-and boundary-based metrics illustrates that the amalgamation of hybrid learning with genetic optimisation and federated training enhances robustness in heterogeneous environments while maintaining data confidentiality,thus providing an efficient approach for secure multi-centre cardiac MRI segmentation. 展开更多
关键词 SDG 3 cardiovascular imaging segmentation ensemble deep learning genetic algorithm federated learning privacy-preserving AI
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A Comprehensive Survey on Blockchain-Enabled Techniques and Federated Learning for Secure 5G/6G Networks:Challenges,Opportunities,and Future Directions 认领 引用
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作者 Muhammad Asim Abdelhamied A.Ateya +4 位作者 Mudasir Ahmad Wani Gauhar Ali Mohammed ElAffendi Ahmed A.Abd El-Latif Reshma Siyal 《Computers, Materials & Continua》 SCIE EI 2026年第3期117-161,共45页
The growing developments in 5G and 6G wireless communications have revolutionized communications technologies,providing faster speeds with reduced latency and improved connectivity to users.However,it raises significa... The growing developments in 5G and 6G wireless communications have revolutionized communications technologies,providing faster speeds with reduced latency and improved connectivity to users.However,it raises significant security challenges,including impersonation threats,data manipulation,distributed denial of service(DDoS)attacks,and privacy breaches.Traditional security measures are inadequate due to the decentralized and dynamic nature of next-generation networks.This survey provides a comprehensive review of how Federated Learning(FL),Blockchain,and Digital Twin(DT)technologies can collectively enhance the security of 5G and 6G systems.Blockchain offers decentralized,immutable,and transparent mechanisms for securing network transactions,while FL enables privacy-preserving collaborative learning without sharing raw data.Digital Twins create virtual replicas of network components,enabling real-time monitoring,anomaly detection,and predictive threat analysis.The survey examines major security issues in emerging wireless architectures and analyzes recent advancements that integrate FL,Blockchain,and DT to mitigate these threats.Additionally,it presents practical use cases,synthesizes key lessons learned,and identifies ongoing research challenges.Finally,the survey outlines future research directions to support the development of scalable,intelligent,and robust security frameworks for next-generation wireless networks. 展开更多
关键词 5G/6G blockchain federated learning edge computing security
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Federated Multi-Label Feature Selection via Dual-Layer Hybrid Breeding Cooperative Particle Swarm Optimization with Manifold and Sparsity Regularization 认领 引用
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作者 Songsong Zhang Huazhong Jin +5 位作者 Zhiwei Ye Jia Yang Jixin Zhang Dongfang Wu Xiao Zheng Dingfeng Song 《Computers, Materials & Continua》 SCIE EI 2026年第1期1141-1159,共19页
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
关键词 Multi-label feature selection federated learning manifold regularization sparse constraints hybrid breeding optimization algorithm particle swarm optimizatio algorithm privacy protection
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Federated Dynamic Aggregation Selection Strategy-Based Multi-Receptive Field Fusion Classification Framework for Point Cloud Classification 认领 引用
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作者 Yuchao Hou Biaobiao Bai +3 位作者 Shuai Zhao Yue Wang Jie Wang Zijian Li 《Computers, Materials & Continua》 SCIE EI 2026年第2期1889-1918,共30页
Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to priva... Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to privacy leaks.Federated learning provides an effective solution to data leakage by eliminating the need for data transmission,relying instead on the exchange of model parameters.However,the uneven distribution of client data can still affect the model’s ability to generalize effectively.To address these challenges,we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework(FDASS-MRFCF).Specifically,we tackle these challenges with two key innovations:(1)During the client local training phase,we propose a Multi-Receptive Field Fusion Classification Model(MRFCM),which captures local and global structures in point cloud data through dynamic convolution and multi-scale feature fusion,enhancing the robustness of point cloud classification.(2)In the server aggregation phase,we introduce a Federated Dynamic Aggregation Selection Strategy(FDASS),which employs a hybrid strategy to average client model parameters,skip aggregation,or reallocate local models to different clients,thereby balancing global consistency and local diversity.We evaluate our framework using the ModelNet40 and ShapeNetPart benchmarks,demonstrating its effectiveness.The proposed method is expected to significantly advance the field of point cloud classification in a secure environment. 展开更多
关键词 Point cloud classification federated learning multi-receptive field fusion dynamic aggregation
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CASBA:Capability-Adaptive Shadow Backdoor Attack against Federated Learning 认领 引用
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作者 Hongwei Wu Guojian Li +2 位作者 Hanyun Zhang Zi Ye Chao Ma 《Computers, Materials & Continua》 SCIE EI 2026年第3期1139-1163,共25页
Federated Learning(FL)protects data privacy through a distributed training mechanism,yet its decentralized nature also introduces new security vulnerabilities.Backdoor attacks inject malicious triggers into the global... Federated Learning(FL)protects data privacy through a distributed training mechanism,yet its decentralized nature also introduces new security vulnerabilities.Backdoor attacks inject malicious triggers into the global model through compromised updates,posing significant threats to model integrity and becoming a key focus in FL security.Existing backdoor attack methods typically embed triggers directly into original images and consider only data heterogeneity,resulting in limited stealth and adaptability.To address the heterogeneity of malicious client devices,this paper proposes a novel backdoor attack method named Capability-Adaptive Shadow Backdoor Attack(CASBA).By incorporating measurements of clients’computational and communication capabilities,CASBA employs a dynamic hierarchical attack strategy that adaptively aligns attack intensity with available resources.Furthermore,an improved deep convolutional generative adversarial network(DCGAN)is integrated into the attack pipeline to embed triggers without modifying original data,significantly enhancing stealthiness.Comparative experiments with Shadow Backdoor Attack(SBA)across multiple scenarios demonstrate that CASBA dynamically adjusts resource consumption based on device capabilities,reducing average memory usage per iteration by 5.8%.CASBA improves resource efficiency while keeping the drop in attack success rate within 3%.Additionally,the effectiveness of CASBA against three robust FL algorithms is also validated. 展开更多
关键词 Federated learning backdoor attack generative adversarial network adaptive attack strategy distributed machine learning
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FRF-BiLSTM:Recognising and Mitigating DDoS Attacks through a Secure Decentralized Feature Optimized Federated Learning Approach 认领 引用
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作者 Sushruta Mishra Sunil Kumar Mohapatra +2 位作者 Kshira Sagar Sahoo Anand Nayyar Tae-Kyung Kim 《Computers, Materials & Continua》 SCIE EI 2026年第3期1118-1138,共21页
With an increase in internet-connected devices and a dependency on online services,the threat of Distributed Denial of Service(DDoS)attacks has become a significant concern in cybersecurity.The proposed system follows... With an increase in internet-connected devices and a dependency on online services,the threat of Distributed Denial of Service(DDoS)attacks has become a significant concern in cybersecurity.The proposed system follows a multi-step process,beginning with the collection of datasets from different edge devices and network nodes.To verify its effectiveness,experiments were conducted using the CICDoS2017,NSL-KDD,and CICIDS benchmark datasets alongside other existing models.Recursive feature elimination(RFE)with random forest is used to select features from the CICDDoS2019 dataset,on which a BiLSTM model is trained on local nodes.Local models are trained until convergence or stability criteria are met while simultaneously sharing the updates globally for collaborative learning.A centralised server evaluates real-time traffic using the global BiLSTM model,which triggers alerts for potential DDoS attacks.Furthermore,blockchain technology is employed to secure model updates and to provide an immutable audit trail,thereby ensuring trust and accountability among network nodes.This research introduces a novel decentralized method called Federated Random Forest Bidirectional Long Short-Term Memory(FRF-BiLSTM)for detecting DDoS attacks,utilizing the advanced Bidirectional Long Short-Term Memory Networks(BiLSTMs)to analyze sequences in both forward and backward directions.The outcome shows the proposed model achieves a mean accuracy of 97.1%with an average training delay of 88.7 s and testing delay of 21.4 s.The model demonstrates scalability and the best detection performance in large-scale attack scenarios. 展开更多
关键词 Bi-directional long short-term memory network distributed denial of service(DDoS) cybersecurity federated learning random forest
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A similarity-guided dynamic adjustment federated learning framework for multicenter keratitis diagnosis 认领 引用
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作者 Jiang Jiewei Cui Yiwei +3 位作者 Yao Qihai Wang Ning Li Kuan Li Zhongwen 《High Technology Letters》 EI CAS 2026年第1期1-10,共10页
Keratitis is a common ophthalmic disease associated with a high risk of blindness.Although deep learning(DL) based on slit-lamp images has shown great promise for automatic keratitis diagnosis,data heterogeneity and p... Keratitis is a common ophthalmic disease associated with a high risk of blindness.Although deep learning(DL) based on slit-lamp images has shown great promise for automatic keratitis diagnosis,data heterogeneity and privacy constraints hinder data sharing,limiting model generalization across multiple medical centers.To address these challenges,we propose a similarity-guided dynamic adjustment federated learning algorithm for automated keratitis diagnosis(SDAFL_AKD).SDAFL_AKD introduces a similarity-based regularization term during local model updates to alleviate catastrophic forgetting and employs a performance-driven dynamic aggregation mechanism on the server-side to adaptively weight client contributions,thereby enhancing global model robustness under non-independent and identically distributed(Non-IID) conditions.The framework is evaluated on slit-lamp images collected from four independent data sources encompassing keratitis,normal cornea,and other cornea abnormalities,and compared with Fed Avg,model-contrastive federated learning(MOON),stochastic controlled averaging for federated learning(SCAFFOLD) and single-center baseline models.Experimental results demonstrate that SDAFL_AKD consistently outperforms conventional methods,achieving average accuracies of 97.95% on a balanced dataset and 86.05% on an imbalanced smart phone-acquired dataset.Ablation studies further confirm the synergistic benefits of the similarity(SIM) and dynamic aggregation(DA) modules in improving multi-category recognition and generalization.These findings indicate the effectiveness of SDAFL_AKD for keratitis diagnosis under data heterogeneous and privacy-constrained conditions,providing a scalable solution for collaborative ophthalmic image analysis across institutions. 展开更多
关键词 federated learning keratitis diagnosis deep learning data heterogeneity dynamic aggregation
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Communication-efficient federated multi-domain learning for network anomaly detection 认领 引用
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作者 Chunjiong Zhang Gaoyang Shan Byeong-hee Roh 《Digital Communications and Networks》 SCIE EI CSCD 2026年第3期472-481,共10页
Efficient and accurate anomaly detection in a network is of great significance for maintaining network and device security.Most anomaly detection methods assume that different anomalous network data distributions are ... Efficient and accurate anomaly detection in a network is of great significance for maintaining network and device security.Most anomaly detection methods assume that different anomalous network data distributions are the same or similar and ignore data privacy preservation.In this paper,a novel Federated Learning(FL)is proposed that it can quickly detect different types of anomalies in Non-Independent and Identically Distributed(Non-IID)data.First,we design a multi-domain machine learning model for multi-domain data,named Aegean,which consists of two modules:an ensemble AutoEncoder(AE)and a Generative Adversarial Network(GAN).Second,because data from different domains are non-IID,we model the anomaly detection problem as a dual problem,which can be recast as a robust optimization problem.The robust optimization problem is non-convex and therefore difficult to solve.As a remedy,we formulate and solve a dual problem by taking the Lagrangian dual function of the original problem.Experiments demonstrate that Aegean significantly outperforms the current state-of-the-art methods,with a 16%F1 score improvement over that of a One-Class Support Vector Machine(OCSVM).The designed FL significantly reduces the communication overhead of FedAvg without sacrificing anomaly detection performance. 展开更多
关键词 Federated learning Multi-domain Network anomaly detection Communication
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