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• Phase-Adaptive Federated Learning for Privacy-Preserving Personalized Travel Itinerary Generation
  We propose Phase-Adaptive Federated Learning (PAFL), a novel framework for privacy-preserving personalized travel itinerary generation that dynamically balances privacy and utility through a phase-dependent aggregation mechanism inspired by phase-change materi…
  openalex Xiaolong Chen, Hongfeng Zhang, Cora Un In Wong 2025-06-02 置信度 0.72 Computer sciencePhase (matter)Internet privacyChemistryOrganic chemistry
• DRACO: Decentralized Asynchronous Federated Learning Over Row-Stochastic Wireless Networks
  Emerging technologies and use cases, such as smart Internet of Things (IoT), Internet of Agents, and Edge AI, have generated significant interest in training neural networks over fully decentralized, serverless networks. A major obstacle in this context is ens…
  openalex Eunjeong Jeong, Marios Kountouris 2025-01-01 置信度 0.72 Asynchronous communicationComputer scienceWirelessDistributed computingWireless network
• ADVANCEMENTS IN FEDERATED LEARNING FOR SECURE DATA SHARING IN FINANCIAL SERVICES
  This paper explores the application of Federated Learning (FL) in the financial sector, focusing on enhancing security and privacy in key areas such as fraud detection, Anti-Money Laundering (AML) compliance, and biometric authentication systems. FL enables co…
  openalex Nkem Belinda Unuigbokhai, Godfrey Oise, Babalola Eyitemi Akilo, Onyemaechi Clement Nwabuokei 等 2025-05-31 置信度 0.72 Data sharingBusinessFinanceComputer scienceFinancial services
• pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning
  Federated learning (FL) is an emerging machine learning paradigm in which a central server coordinates multiple participants (clients) collaboratively to train on decentralized data. In practice, FL often faces statistical, system, and model heterogeneities, w…
  openalex Liping Yi, Han Yu, Gang Wang, Xiaoguang Liu 等 2023-10-20 置信度 0.72 Computer scienceFederated learningComputer architectureArtificial intelligence
• Enhancing agricultural commodity price forecasting with deep learning
  Accurate forecasting of agricultural commodity prices is essential for market planning and policy formulation, especially in agriculture-dependent economies like India. Price volatility, driven by factors such as weather variability and market demand fluctuati…
  openalex R. L. Manogna, Vijay Dharmaji, S Sarang 2025-07-01 置信度 0.72 Autoregressive integrated moving averageDeep learningArtificial intelligenceMean absolute percentage errorMean squared error
• FedMEM: Adaptive Personalized Federated Learning Framework for Heterogeneous Mobile Edge Environments
  With the growth of the Internet of Things (IoT) and communication technologies, edge devices have become more diverse. This diversity has increased the computational load on these systems and led to differences between devices. In mobile edge computing, variat…
  openalex Chen Ximing, He Xilong, Cheng Du, WU Tie-jun 等 2025-04-14 置信度 0.72 Computer scienceEnhanced Data Rates for GSM EvolutionPersonalized learningHuman–computer interactionAdaptive learning
• Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction
  Federated learning is known for its capability to safeguard the participants' data privacy.However, recently emerged model inversion attacks (MIAs) have shown that a malicious parameter server can reconstruct individual users' local data samples from model upd…
  openalex Shanghao Shi, Ning Wang, Xiao Yang, Chaoyu Zhang 等 2025-01-01 置信度 0.72 ScalabilityComputer scienceInversion (geology)Scale (ratio)Space (punctuation)
• Quantum-Driven Reinforcement Learning for Spectral Energy Optimization in Massive MIMO Hybrid Beamforming for 6G
  Abstract The evolution of 6G wireless networks demands highly efficient beamforming strategies to optimize spectral and energy efficiency in massive MIMO systems. This study introduces a Quantum-Driven Reinforcement Learning (QDRL) framework for Spectral Energ…
  openalex R. Krishnamoorthy, M. Amina Begum, Lakshmana Phaneendra Maguluri, Maha Abdelhaq 等 2025-10-01 置信度 0.72 Computer scienceReinforcement learningMIMOBeamformingEnergy consumption
• Emerging role of generative AI in renewable energy forecasting and system optimization
  • Generative AI improves RES forecasting accuracy by up to 25 %. • GANs and VAEs optimize microgrid and storage operations. • Federated learning enables privacy-preserving energy model training. • Black-box models pose interpretability and regulatory challenge…
  openalex Erdiwansyah Erdiwansyah, Rizalman Mamat, Syafrizal Syafrizal, Mohd Fairusham Ghazali 等 2025-07-15 置信度 0.72 Renewable energyGenerative grammarComputer scienceArtificial intelligenceEngineering
• Advancements in Small-Object Detection (2023–2025): Approaches, Datasets, Benchmarks, Applications, and Practical Guidance
  Small-object detection (SOD) remains an important and growing challenge in computer vision and is the backbone of many applications, including autonomous vehicles, aerial surveillance, medical imaging, and industrial quality control. Small objects, in pixels, …
  openalex Ali Aldubaikhi, Sarosh Patel 2025-11-07 置信度 0.72 Computer scienceRobustness (evolution)Software deploymentData scienceArtificial intelligence
• UAV-Assisted Federated Learning With Robust Resource and Trajectory Optimization Under Location Uncertainties
  Federated learning (FL) has emerged as a promising solution to facilitate the deployment of artificial intelligence (AI) on wireless devices. However, heterogeneity of wireless devices, including disparities in computation capabilities, data sizes, and energy …
  openalex Chen Wang, Xiao Tang, Zehui Xiong, Daosen Zhai 等 2025-09-26 置信度 0.72 Computer scienceCoordinate descentTrajectoryComputationWireless
• Machine learning and deep learning in medicine and neuroimaging
  Artificial intelligence is the science and engineering of machines that can mimic human intelligence. Machine learning is the subfield of artificial intelligence in which computers have the ability to learn and iteratively improve their performance without bei…
  openalex Iván Sánchez Fernández, Jurriaan M. Peters 2023-02-07 置信度 0.72 Artificial intelligenceDeep learningComputer scienceMachine learningConvolutional neural network
• Secure blockchain integrated deep learning framework for federated risk-adaptive and privacy-preserving IoT edge intelligence sets
  An enormous demand for a secure, scalable, intelligent edge computing framework has emerged for the exponentially increasing number of Internet of Things (IoT) devices for any substrate of modern digital infrastructure. These edge nodes distributed across hete…
  openalex K. Swathi, Putta Durga, K. Venkata Prasad, A Krishna Chaitanya 等 2025-11-20 置信度 0.72 Computer scienceEdge computingDeep learningBlockchainEdge device
• Federated Transfer Learning for Tomato Leaf Disease Detection Using Neuro-Graph Hybrid Model
  Plant diseases are currently a major threat to agricultural economies and food availability, having a negative environmental impact. Despite being a promising line of research, current approaches struggle with poor cross-site generalization, limited labels and…
  openalex Aurora-Felicia Cristea, Ciprian Dobre 2025-12-15 置信度 0.72 Robustness (evolution)Computer scienceMachine learningTransfer of learningArtificial intelligence
• Blockchain-based personalized federated learning framework for drug recommendation systems resilient to model poisoning
  Abstract Federated learning enables multiple healthcare entities to collaboratively train a global model while ensuring patient data privacy through local model training without sharing raw data. However, FL remains vulnerable to adversarial attacks such as mo…
  openalex Sina Apak, İsmail Tuncer Değim, Samaneh Zahertar 2026-01-01 置信度 0.72 Computer scienceFederated learningRobustness (evolution)Software deploymentRecommender system
• Automated machine learning with interpretation: A systematic review of methodologies and applications in healthcare
  Abstract Machine learning (ML) has achieved substantial success in performing healthcare tasks in which the configuration of every part of the ML pipeline relies heavily on technical knowledge. To help professionals with borderline expertise to better use ML t…
  openalex Han Yuan, Kunyu Yu, Feng Xie, M. Liu 等 2024-08-27 置信度 0.72 Health careArtificial intelligenceComputer scienceInterpretation (philosophy)Data science
• Federated Learning‐Based Intrusion Detection Systems for Massive IoT
  Despite the significant benefits that the 6G-enabled massive Internet of Things (IoT) applications will bring to the economy and society in the coming years, it is expected that the exponential increase in the number and the diversity of IoT devices and connec…
  openalex Filippos Pelekoudas‐Oikonomou, Parya Haji Mirzaee, Waleed Hathal, Γεώργιος Μαντάς 等 2024-12-13 置信度 0.72 Intrusion detection systemComputer scienceInternet of ThingsData miningComputer security
• Robust Federated Learning Against Data Poisoning Attacks: Prevention and Detection of Attacked Nodes
  Federated learning (FL) enables collaborative model building among a large number of participants without sharing sensitive data to the central server. Because of its distributed nature, FL has limited control over local data and the corresponding training pro…
  openalex Pretom Roy Ovi, Aryya Gangopadhyay 2025-07-25 置信度 0.72 Computer scienceRobustness (evolution)Training setFederated learningMachine learning
• The Sugeno Integral Used for Federated Learning with Uncertainty for Unbalanced Data
  Data is crucial in the digital economy. Many businesses collect and use their data to enhance their performance. However, limited data or low data quality can hinder model development, particularly in dynamic environments. To overcome this, companies collectin…
  openalex Anna Wilbik, Barbara Pȩkala, Jarosław Szkoła, Krzysztof Dyczkowski 2023-08-13 置信度 0.72 Federated learningComputer scienceMechanism (biology)Data sharingQuality (philosophy)
• Privacy-Preserving Diabetes and Heart Disease Prediction via Federated Learning and WCO
  Diabetes, afflicting 537 million worldwide, is a prevalent and lethal non-communicable ailment. Its onset, influenced by factors like obesity and family history, manifests symptoms such as frequent urination. Long-term complications encompass heart, kidney, an…
  openalex Sachikanta Dash, Sasmita Padhy, Preetam Suman, Sandip Mal 等 2025-08-23 置信度 0.72 Computer scienceDiabetes mellitusArtificial intelligenceMachine learningMedicine
• Federated Learning for Wireless Communications
  In the past few years, machine learning (ML) techniques have been introduced for the physical layer applications in wireless communications. In contrast to employing centralized learning (CL) techniques, federated learning (FL) presents lower communication ove…
  openalex Ahmet M. Elbir, Wei Shi 2024-12-13 置信度 0.72 WirelessComputer scienceTelecommunicationsComputer network
• Certifying the Right to Be Forgotten: Primal–Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning
  Federated unlearning has become an attractive approach to address privacy concerns in collaborative machine learning, for situations when sensitive data are remembered by AI models during the machine learning process. It enables the removal of specific data in…
  openalex Yu Jiang, Xindi Tong, Ziyao Liu, Xiaoxi Zhang 等 2025-01-01 置信度 0.72 Computer scienceOverhead (engineering)Federated learningSample (material)Feature (linguistics)
• DFCA: Decentralized Federated Clustering Algorithm
  Clustered Federated Learning has emerged as an effective approach for handling heterogeneous data across clients by partitioning them into clusters with similar or identical data distributions. However, most existing methods, including the Iterative Federated …
  openalex Jonas Kirch, S. Becker, Tiago Koketsu Rodrigues, Stefan Harmeling 2026-03-02 置信度 0.72 Computer scienceCluster analysisBottleneckFederated learningRobustness (evolution)
• Novel Federated Graph Contrastive Learning for IoMT Security: Protecting Data Poisoning and Inference Attacks
  Malware evolution presents growing security threats for resource-constrained Internet of Medical Things (IoMT) devices. Conventional federated learning (FL) often suffers from slow convergence, high communication overhead, and fairness issues in dynamic IoMT e…
  openalex Amarudin Daulay, Kalamullah Ramli, Ruki Harwahyu, Taufik Hidayat 等 2025-07-31 置信度 0.72 Computer scienceInferenceComputer securityGraphTheoretical computer science
• Trusted Data Sharing Mechanism Based on Blockchain and Federated Learning in Space‐Air‐Ground Integrated Networks
  Network data is distributed data on electricity, with the explosive growth of network data, and it has become an inevitable trend of network development to synergized and shared crossdomain scattered data and enhances the value transmission of network data. Fe…
  openalex Da Li, Qinglei Guo, Chao Yang, Han Yan 2022-01-01 置信度 0.72 Computer scienceData sharingComputer securityTrusted third partyIncentive
• Deep Learning Approaches for EEG-Motor Imagery-Based BCIs: Current Models, Generalization Challenges, and Emerging Trends
  This study critically examines the evolution of deep learning (DL) for electroencephalogram (EEG) based motor imagery (MI) decoding with a focus on real-time Brain Computer Interfaces (BCIs) development. Prior studies often prioritize accuracy in isolation, ne…
  openalex Aaqib Raza, Mohd Zuki Yusoff 2025-01-01 置信度 0.72 Motor imageryBrain–computer interfaceElectroencephalographyComputer scienceGeneralization
• The blockchain‐based privacy‐preserving searchable attribute‐based encryption scheme for federated learning model in IoMT
  Abstract Federated learning enables training healthcare diagnostic models across multiple decentralized devices containing local private health data samples, without transferring data to a central server, providing privacy‐preserving services for healthcare pr…
  openalex Ziyu Zhou, Na Wang, Jianwei Liu, Junsong Fu 等 2024-08-20 置信度 0.72 Computer scienceBlockchainScheme (mathematics)EncryptionLearning with errors
• FedNolowe: A normalized loss-based weighted aggregation strategy for robust federated learning in heterogeneous environments
  Federated Learning supports collaborative model training across distributed clients while keeping sensitive data decentralized. Still, non-independent and identically distributed data pose challenges like unstable convergence and client drift. We propose Feder…
  openalex Duy-Dong Le, Nguyen Huynh Tuong, Anh-Khoa Tran, Minh-Son Dao 等 2025-08-14 置信度 0.72 Computer scienceFederated learningData aggregatorArtificial intelligenceComputer network
• Blockchain-Enabled Hierarchical Federated Learning Framework for Anomaly Detection in IoT Systems
  The rapid expansion of the Internet of Things (IoT) across domains such as industrial automation, smart healthcare, and intelligent transportation has intensified security challenges, particularly in terms of detecting anomalies across large-scale, heterogeneo…
  openalex Haya Alharthi, Suhair Alshehri, Manal Kalkatawi 2025-12-11 置信度 0.72 Federated learningComputer scienceInternet of ThingsAnomaly detectionTrustworthiness
• FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence
  Edge Intelligence (EI) enables Artificial Intelligence (AI) applications to run at the edge, where data analysis and decision-making can be performed in real-time and close to data sources. To protect data privacy and unify data silos distributed among end dev…
  openalex Zhiyuan Wu, Sheng Sun, Yuwei Wang, Min Liu 等 2023-06-13 置信度 0.72 Computer scienceCacheOverhead (engineering)Enhanced Data Rates for GSM EvolutionArchitecture
• Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge
  Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Fe…
  openalex Maximilian Zenk, Ujjwal Baid, Sarthak Pati, Akis Linardos 等 2025-07-07 置信度 0.72 BenchmarkingBenchmark (surveying)Computer scienceSegmentationArtificial intelligence
• Network-based intrusion detection using deep learning technique
  A high growth rate in network traffic and the complexity of cyber threats have made it necessary to create more effective and flexible intrusion detection systems. Most traditional Network-based Intrusion Detection Systems (NIDS) can become weak at detecting n…
  openalex Muhammad Farhan, Hafiz Waheed ud din, S. M. Wazid Ullah, Muhammad Sajjad Hussain 等 2025-07-15 置信度 0.72 Computer scienceIntrusion detection systemArtificial intelligenceDeep learningIntrusion
• Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning With Adaptive Quantization and Differential Privacy
  Federated learning (FL) is a distributed machine learning method where multiple devices collaboratively train a model under the management of a central server without sharing underlying data. One of the key challenges of FL is the communication bottleneck caus…
  openalex Emre Ardıç, Yakup Genç 2025-01-01 置信度 0.72 Differential privacyComputer scienceInformation privacyQuantization (signal processing)Privacy software
• Recent Methodological Advances in Federated Learning for Healthcare
  openalex Fan Zhang, Daniel Kreuter, Yi‐Chen Chen, Sören Dittmer 等 2023-10-04 置信度 0.72 Computer scienceFederated learningPoolingHealth careCompromise
• Advancing Fusion with Machine Learning Research Needs Workshop Report
  Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and …
  openalex D.A. Humphreys, Ana Kupresanin, Mark D. Boyer, J.M. Canik 等 2020-08-01 置信度 0.72 Artificial intelligenceComputer scienceMachine learningIdentification (biology)Robotics
• A Survey on Collaborative Learning for Intelligent Autonomous Systems
  This survey examines approaches to promote Collaborative Learning in distributed systems for emergent Intelligent Autonomous Systems (IAS). The study involves a literature review of Intelligent Autonomous Systems based on Collaborative Learning, analyzing aspe…
  openalex Julio César Santos dos Anjos, Kassiano J. Matteussi, Fernanda C. Orlandi, Jorge Luís Victória Barbosa 等 2023-09-28 置信度 0.72 Computer scienceSoftware deploymentBig dataKnowledge managementCollaborative learning
• LLM-Based Agents for Tool Learning: A Survey
  Abstract Human beings capable of making and using tools can accomplish tasks far beyond their innate abilities, and this paradigm of integration with tools may not be limited to humans themselves. Recently, the large language model (LLM) has demonstrated immen…
  openalex Weikai Xu, Chengrui Huang, Shen Gao, Shuo Shang 2025-06-26 置信度 0.72 Computer scienceData science
• Identifying significant features in adversarial attack detection framework using federated learning empowered medical IoT network security
  The expansion of the Internet of Medical Things (IoHT) presents significant advantages for healthcare over improved data-driven insights and connectivity and offers critical cybersecurity challenges. Attacks are a serious risk for neural network security; rece…
  openalex Sanaa Sharaf, Sameer Nooh 2025-08-26 置信度 0.72 Adversarial systemComputer scienceInternet of ThingsComputer securityNetwork security
• Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations
  Background/Objectives: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neuroscience insights with advanced algorithmic methods in pursuit of a…
  openalex Constantinos Halkiopoulos, Evgenia Gkintoni, Anthimos Aroutzidis, Hera Antonopoulou 2025-02-13 置信度 0.72 NeuroimagingCognitionModalitiesComputer scienceDeep learning
• Adversarial machine learning: a review of methods, tools, and critical industry sectors
  Abstract The rapid advancement of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), has produced high-performance models widely used in various applications, ranging from image recognition and chatbots to autonomous drivi…
  openalex Sotiris Pelekis, Thanos Koutroubas, Afroditi Blika, Anastasis Berdelis 等 2025-05-02 置信度 0.72 Computer scienceAdversarial systemArtificial intelligenceMachine learningEngineering management
• Enhancing Efficiency in Multidevice Federated Learning through Data Selection
  Ubiquitous wearable and mobile devices provide access to a diverse set of data. However, the mobility demand for our devices naturally imposes constraints on their computational and communication capabilities. A solution is to locally learn knowledge from data…
  openalex Fan Mo, Mohammad Malekzadeh, Soumyajit Chatterjee, Fahim Kawsar 等 2022-11-08 置信度 0.72 Computer scienceBenchmark (surveying)Partition (number theory)Federated learningProcess (computing)
• Digital Twin: Enabling Technologies, Challenges and Open Research
  Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digi…
  openalex Aidan Fuller, Zhong Fan, Charles Day, Chris Barlow 2020-01-01 置信度 0.72 Computer scienceOpen researchWorld Wide Web
• An Asynchronous Federated Learning Aggregation Method Based on Adaptive Differential Privacy
  Federated learning is a distributed machine learning technique that allows multiple devices to collaborate on learning a shared model without exchanging data. It can be used to improve model accuracy while protecting user privacy. However, traditional federate…
  openalex Jiawen Wu, Geming Xia, Hongwei Huang, Chaodong Yu 等 2025-07-16 置信度 0.72 Differential privacyAsynchronous communicationComputer scienceFederated learningAsynchronous learning
• Federated learning for automotive applications
  This paper presents and evaluates a new distributed learning technology, called federated learning, and its applications in automotive systems. We review and classify existing approaches to federated learning, focusing on its implementation in connected vehicl…
  openalex William Lindskog-Münzing, Christian Prehofer 2025-05-23 置信度 0.72 Automotive industryComputer scienceBusinessManufacturing engineeringEngineering
• Collaboration of IoT devices in smart home scenarios: algorithm research based on graph neural networks and federated learning
  Traditional IoT device collaboration is usually static and cannot adjust the collaboration mode between devices according to various changes, which limits work efficiency. To this end, an IoT device collaboration optimization algorithm based on graph neural ne…
  openalex Yuanquan Zhong 2025-01-09 置信度 0.72 Computer scienceInternet of ThingsArtificial neural networkGraphArtificial intelligence
• DRL-Based Joint Aggregation Frequency and Edge Association for Energy-Efficient Hierarchical Federated Learning
  Hierarchical Federated Learning (HFL) has been proposed to achieve large-scale model training and more efficient communication, surpassing conventional Federated Learning (FL). However, inappropriate aggregation frequency and edge association in HFL result in …
  openalex Yijing Ren, Changxiang Wu, Daniel K. C. So, Jie Tang 2025-04-09 置信度 0.72 Computer scienceJoint (building)Association (psychology)Enhanced Data Rates for GSM EvolutionArtificial intelligence
• Asynchronous Byzantine Federated Learning
  Federated learning (FL) enables a set of geographically distributed clients to collectively train a model through a server. Classically, the training process is synchronous, but can be made asynchronous to maintain its speed in presence of slow clients and in …
  openalex Bart Cox, Abele Mălan, Lydia Y. Chen, Jérémie Decouchant 2024-06-03 置信度 0.72 Byzantine architectureAsynchronous communicationComputer scienceByzantine fault toleranceAsynchronous learning
• SECURE PREDICTIVE MAINTENANCE FOR INDUSTRIAL SYSTEMS USING FEDERATED LEARNING
  In this paper, a secure and scalable predictive maintenance approach for Industry 4.0 using Federated Learning (FL) and Artifficial Intelligence (AI) is addressed.Unlike other approaches, FL maintains data on the premises, which guarantees privacy and regulato…
  openalex 2025-01-01 置信度 0.72 Predictive maintenanceComputer scienceReliability engineeringEngineering
• When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair
  Software systems have been evolving rapidly and inevitably introducing bugs at an increasing rate, leading to significant maintenance costs. While large language models (LLMs) have demonstrated remarkable potential in enhancing software development and mainten…
  openalex Wenqiang Luo, Jacky Keung, Boyang Yang, He Ye 等 2025-05-01 置信度 0.72 Computer scienceEmpirical researchInternet privacyComputer securityPhilosophy
• FLUID: Dynamic Model-Agnostic Federated Learning with Pruning and Knowledge Distillation for Maritime Predictive Maintenance
  Predictive maintenance (PdM) is vital to maritime operations; however, the traditional deep learning solutions currently offered heavily depend on centralized data aggregation, which is impractical under the limited connectivity, privacy concerns, and resource…
  openalex Alexandros Kalafatelis, Angeliki Pitsiakou, Νικόλαος Νομικός, Nikolaos Tsoulakos 等 2025-08-15 置信度 0.72 PruningDistillationComputer sciencePredictive maintenanceMachine learning
• Federated Learning and Blockchain Framework for Scalable and Secure IoT Access Control
  The increasing deployment of Internet of Things (IoT) devices has introduced significant security challenges, including identity spoofing, unauthorized access, and data integrity breaches. Traditional security mechanisms rely... | Find, read and cite all the r…
  openalex Ammar Odeh, Anas Abu Taleb 2025-01-01 置信度 0.72 BlockchainScalabilityInternet of ThingsComputer scienceAccess control
• Latency Analysis of UAV-Assisted Vehicular Communications Using Personalized Federated Learning with Attention Mechanism
  In this paper, unmanned aerial vehicle (UAV)-assisted vehicular communications are investigated to minimize latency and maximize the utilization of available UAV battery power. As communication and cooperation among UAV and vehicles is frequently required, a v…
  openalex Abhishek Gupta, Xavier Fernando 2025-07-15 置信度 0.72 Latency (audio)Mechanism (biology)Computer scienceComputer networkHuman–computer interaction
• Comparative analysis of deep learning architectures in solar power prediction
  Integrating renewable energy sources into the electricity grid requires accurate forecasts of solar power production. With the aim of enhancing the accuracy and reliability of forecasts, this study presents a comprehensive comparative analysis of eight state-o…
  openalex Montaser Abdelsattar, Mohamed Mostafa A. Azim, Ahmed AbdelMoety, Ahmed Emad-Eldeen 2025-08-28 置信度 0.72 AutoencoderComputer scienceConvolutional neural networkArtificial intelligenceAlgorithm
• A deep learning/machine learning approach for anomaly based network intrusion detection
  Introduction: The increasing complexity and frequency of cybersecurity threats necessitate the development of advanced detection systems capable of identifying both known and emerging attacks. In this study, we present a hybrid anomaly-based Network Intrusion …
  openalex Reem Al-Muhanna, Samia Dardouri 2025-09-09 置信度 0.72 Intrusion detection systemGeneralizability theoryRobustness (evolution)Anomaly detectionComputer science
• Deep learning for sustainable development across climate, energy, agriculture and urban systems
  Deep learning (DL) has emerged as a transformative paradigm for addressing the multidimensional and data-intensive challenges of sustainable development. The purpose of this review is to examine how DL contributes to four critical domains-climate action, susta…
  openalex Harshit Sharma, Simran Kaur 2025-12-22 置信度 0.72 Transformative learningSustainabilitySustainable developmentDeep learningCitizen journalism
• Decentralized AI at the Edge: Federated Learning, Quantum Optimization and IoT Scalability
  Decentralized artificial intelligence (AI) at the edge marks a revolutionary evolution in computing, enabling efficient, privacy-preserving, and scalable solutions tailored for the Internet of Things (IoT). This paper integrates cutting-edge advancements in fe…
  openalex Surya Kiran, Arjun Kumar, Swathi Chukkala 2025-03-10 置信度 0.72 ScalabilityComputer scienceEnhanced Data Rates for GSM EvolutionInternet of ThingsDistributed computing
• A Survey of Clustering Federated Learning in Heterogeneous Data Scenarios
  Federated learning, as a collaborative training paradigm that preserves raw data privacy, offers an effective solution for data protection concerns. However, its practical implementation faces significant challenges due to data heterogeneity. This heterogeneit…
  openalex Entuo Liu, Wentong Yang, Yonggen Gu, Wei Long 等 2025-04-30 置信度 0.72 Cluster analysisComputer scienceData miningData scienceMachine learning
• Resource-Constrained Decentralized Federated Learning via Personalized Event-Triggering
  Federated learning (FL) is a popular technique for distributing machine learning (ML) across a set of edge devices. In this paper, we study fully decentralized FL, where in addition to devices conducting training locally, they carry out model aggregations via …
  openalex Shahryar Zehtabi, Seyyedali Hosseinalipour, Christopher G. Brinton 2022-11-23 置信度 0.72 Asynchronous communicationComputer scienceDistributed computingEnhanced Data Rates for GSM EvolutionConvergence (economics)
• Ultra-Short-Term Distributed Photovoltaic Power Probabilistic Forecasting Method Based on Federated Learning and Joint Probability Distribution Modeling
  The accurate probabilistic forecasting of ultra-short-term power generation from distributed photovoltaic (DPV) systems is of great significance for optimizing electricity markets and managing energy on the user side. Existing methods regarding cluster informa…
  openalex Yübo Wang, Chao Huo, Fei Xu, Libin Zheng 等 2025-01-05 置信度 0.72 Computer scienceProbabilistic logicData miningProbabilistic forecastingJoint probability distribution
• Empowering Federated Graph Rationale Learning with Latent Environments
  The success of Graph Neural Networks (GNNs) in graph classification has heightened interest in explainable GNNs, particularly through graph rationalization. This method aims to enhance GNNs explainability by identifying subgraph structures (i.e., rationales) t…
  openalex Linan Yue, Qi Liu, Yawen Li, Fangzhou Yao 等 2025-04-22 置信度 0.72 Computer scienceGraphData scienceWorld Wide WebTheoretical computer science
• Participant Selection for Efficient and Trusted Federated Learning in Blockchain-Assisted Hierarchical Federated Learning Architectures
  Federated learning has attracted widespread attention due to its strong capabilities of privacy protection, making it a powerful supporting technology for addressing data silos in the future. However, federated learning still lags significantly behind traditio…
  openalex Peng Liu, Lili Jia, Yang Xiao 2025-02-08 置信度 0.72 BlockchainComputer scienceFederated learningSelection (genetic algorithm)Direct Anonymous Attestation
• Privacy-Preserving Federated Learning for Predictive Maintenance in Smart Manufacturing Networks
  Smart manufacturing environments (digitalized production systems with integrated sensor networks and data analytics capabilities) require advanced predictive maintenance capabilities, yet implementation faces significant barriers due to data privacy concerns a…
  openalex Yulineth Cárdenas Escorcia, Sardor Sabirov, Bakhodir Saydullayev, A. V. Umarov 等 2025-09-08 置信度 0.72 Predictive maintenanceComputer scienceOverhead (engineering)Predictive analyticsDifferential privacy
• HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark
  As AI evolves, collaboration among heterogeneous models helps overcome data scarcity by enabling knowledge transfer across institutions and devices.Traditional Federated Learning (FL) only supports homogeneous models, limiting collaboration among clients with …
  openalex Jianqing Zhang, Xinghao Wu, Yanbing Zhou, Xiaoting Sun 等 2025-08-03 置信度 0.72 Benchmark (surveying)Computer scienceArtificial intelligenceData scienceInformation retrieval
• Quantization-based chained privacy-preserving federated learning
  Federated Learning (FL) is an advanced distributed machine learning framework crucial in protecting data privacy and security. By enabling multiple participants to train models while keeping their data local collaboratively, FL effectively mitigates the risks …
  openalex Ya Liu, Shumin Wu, Yibo Li, Fengyu Zhao 等 2025-05-16 置信度 0.72 Computer scienceFederated learningQuantization (signal processing)World Wide WebInternet privacy
• Federated Learning Approaches for Privacy-Preserving Threat Detection in Smart Home IoT Environments
  Smart home Internet of Things (IoT) environments have become increasingly pervasive, offering convenience and automation while simultaneously introducing new cybersecurity vulnerabilities. Traditional centralized machine learning approaches for threat detectio…
  openalex Chima Nwankwo Idika, Edward Oziegbe Salami 2024-10-30 置信度 0.72 Internet of ThingsInternet privacyComputer securityHome automationComputer science
• Federated Learning for Semantic Communication Based on CNNs and Transformer
  This study focuses on the latest research advancements in the field of semantic communication. Traditional communication systems prioritize the transmission of raw data, whilst semantic communication emphasizes conveying the meaning represented by the data. Ho…
  openalex Shufeng Li, Yujun Cai, Zhaokai Deng, Xinran Ba 等 2025-01-01 置信度 0.72 Computer scienceTransformerConvolutional neural networkArtificial intelligenceSemantic data model
• Federated Multi-Agent DRL for Task Offloading in Vehicular Edge Computing
  With the expansion of vehicle-to-everything (V2X) networks and the rising demand for intelligent services, vehicle edge computing encounters heightened requirements for more efficient task offloading. This study proposes a task offloading technique that utiliz…
  openalex Hongwei Zhao, Yu Li, Zhixi Pang, Zihan Ma 2025-09-01 置信度 0.72 Computer scienceEdge computingTask (project management)Enhanced Data Rates for GSM EvolutionDistributed computing
• Privacy and Trust in Blockchain-Federated Intrusion Detection Systems: Taxonomy, Challenges and Perspectives
  Intrusion Detection Systems (IDS) play a critical role in protecting modern networks, but traditional centralized designs raise serious concerns regarding data privacy, trust, and scalability. Federated Learning (FL) reduces privacy risks through decentralized…
  openalex Cao Yuan, Chin Soon Ku, Rahul Kumar, Arshad Khan 2025-11-02 置信度 0.72 Computer scienceImmutabilityComputer securityTrustworthinessKey (lock)
• FedSteg: Coverless Steganography‐Based Privacy‐Preserving Decentralized Federated Learning
  Federated learning (FL) represents a novel privacy‐preserving learning paradigm that offers a practical solution for distributed privacy preservation. Although privacy‐preserving FL based on homomorphic encryption (HE‐PPFL) exhibits resistance to gradient leak…
  openalex Mengfan Xu, Yaguang Lin 2024-04-29 置信度 0.72 SteganographyComputer scienceBlockchainSteganography toolsComputer security
• Managing Supply and Value Chains in a New Era of Global Trade: The Promise of Federated Learning
  As global trade shifts from an era of efficiency-driven globalisation to a new compliance-centred paradigm, customs administrations face mounting challenges – ranging from forced labour and environmental enforcement to fractured supply chain visibility and esc…
  openalex Alan Bersin, Peter Goodings Swartz, Lars Karlsson 2025-04-29 置信度 0.72 Supply chainValue (mathematics)BusinessInternational tradeComputer science
• Advancements and Challenges in Federated Learning : A Survey
  Machine Learning (ML) has significantly impacted daily life by automating tasks and enhancing decision-making across diverse sectors such as healthcare, finance, and transportation. However, concerns regarding data privacy, particularly in sensitive domains li…
  openalex Vivek Prajapat, Narendra Pal Singh Rathore, Kamal Kumar Sethi, Shiv Shankar Rajput 2024-09-27 置信度 0.72 Computer scienceData science
• HealthGuard: An Intelligent Healthcare System Security Framework Based on Machine Learning
  Utilization of the Internet of Things and ubiquitous computing in medical apparatuses have “smartified” the current healthcare system. These days, healthcare is used for more than simply curing patients. A Smart Healthcare System (SHS) is a network of implante…
  openalex Amit Sundas, Sumit Badotra, Salil Bharany, Ahmad Almogren 等 2022-09-22 置信度 0.72 Computer scienceWearable computerComputer securityInternet of ThingsWearable technology
• Federated Low-Rank Adaptation for Foundation Models: A Survey
  Effectively leveraging private datasets remains a significant challenge in developing foundation models. Federated Learning (FL) has recently emerged as a collaborative framework that enables multiple users to fine-tune these models while mitigating data priva…
  openalex Yiyuan Yang, Guodong Long, Qinghua Lu, Liming Zhu 等 2025-09-01 置信度 0.72 Foundation (evidence)Computer scienceAdaptation (eye)Data scienceKey (lock)
• Integrating reinforcement learning and federated meta-learning for energy-efficient wireless body sensor networks
  Abstract Intra wireless body sensor network (Intra-WBSN) is typically a short range wireless health monitoring network, Intra wireless body sensor network (Intra-WBSN) is typically a short range wireless health monitoring network, Wireless body sensor networks…
  openalex Soufiane Ben Othman 2025-12-01 置信度 0.72 Computer scienceWireless sensor networkComputer networkKey distribution in wireless sensor networksDistributed computing
• Machine Learning for Service Migration: A Survey
  Future communication networks are envisioned to satisfy increasingly granular and dynamic requirements to accommodate the application and user demands. Indeed, novel immersive and mission-critical services necessitate increased computing and network resources,…
  openalex Nassima Toumi, Miloud Bagaa, Adlen Ksentini 2023-01-01 置信度 0.72 Computer scienceOrchestrationService (business)EnablingQuality of service
• Artificial intelligence and students’ cognitive learning outcomes with bibliometric and content analysis for future research agenda
  This study conducted a comprehensive bibliometric and content analysis to explore the integration of artificial intelligence in students’ cognitive learning outcomes. A structured TITLE-ABS-KEY search was performed in the Scopus database using keywords such as…
  openalex Shaukat Rahman Ansari, Ika Nurul Qamari 2025-10-21 置信度 0.72 Transformative learningContent analysisScopusBibliometricsComputer science
• Framework for Addressing Imbalanced Data in Aviation with Federated Learning
  The aviation industry generates vast amounts of data across multiple stakeholders, but critical faults and anomalies occur rarely, creating inherently imbalanced datasets that complicate machine learning applications. Traditional centralized approaches are fur…
  openalex Igor Kabashkin 2025-02-16 置信度 0.72 AviationData scienceComputer scienceKnowledge managementEngineering
• FedMDKGE: Multi-granularity Dynamic Knowledge Graph Embedding in Federated Learning
  As knowledge is time-sensitive, some researchers have started to focus on dynamic knowledge graphs to provide time-dimensioned knowledge content thus reflecting richer information. But they have not yet combined temporal information at different granularities.…
  openalex Wei Huang, Junling Chen, Dexian Wang, Pengfei Zhang 等 2025-06-02 置信度 0.72 GranularityComputer scienceEmbeddingKnowledge graphGraph
• Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring
  The application of Deep Neural Networks (DNNs) for monitoring cyberattacks in Internet of Things (IoT) systems has gained significant attention in recent years. However, achieving optimal detection performance through DNN training has posed challenges due to c…
  openalex Idris Zakariyya, Harsha Kalutarage, M. Omar Al-Kadri 2023-07-20 置信度 0.72 Computer scienceTestbedRobustness (evolution)Artificial intelligenceMemory footprint
• Model Poisoning Attacks to Federated Learning via Multi-Round Consistency
  Model poisoning attacks are critical security threats to Federated Learning (FL). Existing model poisoning attacks suffer from two key limitations: 1) they achieve suboptimal effectiveness when defenses are deployed, and/or 2) they require knowledge of the mod…
  openalex Yueqi Xie, Minghong Fang, Neil Zhenqiang Gong 2024-04-24 置信度 0.72 Consistency (knowledge bases)Computer scienceComputer securityFederated learningDatabase
• Federated Learning for Multi-Disease Ophthalmic Diagnostics Using OCT Angiography
  Purpose: To conduct a comprehensive systematic evaluation of federated learning (FL) strategies for multi-disease retinal classification using OCT angiography (OCTA), implementing a 2-part experimental framework to establish foundational feasibility and optimi…
  openalex Ahammed Sakir Nabil, Sina Gholami, Theodore Leng, Jennifer I. Lim 等 2025-12-19 置信度 0.72 MedicineComputer scienceAngiographyComputer visionArtificial intelligence
• Federated Deep Reinforcement Learning for Privacy-Preserving Robotic-Assisted Surgery
  The integration of Reinforcement Learning (RL) into robotic-assisted surgery (RAS) holds significant promise for advancing surgical precision, adaptability, and autonomous decision-making. However, the development of robust RL models in clinical settings is hi…
  openalex Sana Hafeez, Sundas Rafat Mulkana, Muhammad Ali Imran, Michele Sevegnani 2025-07-21 置信度 0.72 Reinforcement learningComputer scienceHomomorphic encryptionSoftware deploymentMargin (machine learning)
• Personalized Federated Transfer Learning for Building Energy Forecasting via Model Ensemble with Multi-Level Masking in Heterogeneous Sensing Environment
  Effective building energy prediction is essential for optimizing energy management, but existing models struggle with data scarcity and sensor heterogeneity across different buildings. Conventional approaches, including centralized and transfer learning method…
  openalex Hakjae Kim, Sarangerel Dorjgochoo, Hansaem Park, Sung-Ju Lee 2025-04-28 置信度 0.72 Computer scienceMasking (illustration)Transfer of learningEnergy transferFederated learning
• Artificial Intelligence and machine learning in fraud detection for digital payments
  The financial sector has adopted artificial intelligence (AI) and machine learning (ML) for more advanced and real-time fraud detection as a result of the increased threat of fraud brought on by the global surge in digital payments. By using sophisticated algo…
  openalex Alexandre Davitaia 2025-06-13 置信度 0.72 PaymentComputer scienceArtificial intelligenceMachine learningComputer security
• Linking Health Records for Federated Query Processing
  Abstract A federated query portal in an electronic health record infrastructure enables large epidemiology studies by combining data from geographically dispersed medical institutions. However, an individual’s health record has been found to be distributed acr…
  openalex Rinku Dewri, Toan C. Ong, Ramakrishna Thurimella 2016-05-06 置信度 0.72 Computer scienceIdentifierEncryptionInformation retrievalSet (abstract data type)
• Federated Deep Learning for Robust Multi-Modal Biometric Authentication Based on Facial and Eye-Blink Cues
  The increasing demand for secure and user-friendly authentication mechanisms has led to the exploration of biometric systems that leverage unique physiological traits. Among these, face recognition and eye blink detection have emerged as effective and non-intr…
  openalex A. Balaji, Doppalapudi Balanjali, Guntu Subbaiah, Avula Anil Reddy 等 2025-04-14 置信度 0.72 BiometricsComputer scienceSpoofing attackLeverage (statistics)Facial recognition system
• On-Device Federated Learning for Energy-Efficient Smart Irrigation
  This study presents a novel federated learning (FL) methodology implemented directly on STM32-based microcontrollers (MCUs) for energy-efficient smart irrigation. To the best of our knowledge, this is the first work to demonstrate end-to-end FL training and ag…
  openalex Zohra Dakhia, Alessia Lazzaro, Mohamed Riad Sebti, Mariateresa Russo 等 2025-11-02 置信度 0.72 MicrocontrollerComputer scienceMessage queueInferenceSecurity token
• Collision avoidance in UAV swarms: A learning-centric perspective on collaborative intelligence
  As UAV swarm deployments become more prevalent in mission critical domains, collision avoidance remains a key challenge in ensuring safety, coordination, and autonomy at scale. This survey investigates the state of the art in learning based collision avoidance…
  openalex Himadri Sikhar Khargharia, Anis Ouali, Siddhartha Shakya, S. Ahmad 2025-11-08 置信度 0.72 Computer scienceCollision avoidanceSoftware deploymentReinforcement learningScalability
• Review: machine learning approaches for diverse alloy systems
  Abstract The integration of machine learning (ML) into alloy design has revolutionized the discovery and optimization of advanced materials by enabling high-throughput, data-driven methodologies. This review systematically examines recent advancements in ML ap…
  openalex Arafat Rahman, Md Sojib Hossain, Abdullah Siddique 2025-07-17 置信度 0.72 Materials scienceSolid mechanicsAlloyMetallurgyPolymer science
• Federated transfer learning for distributed drought stage prediction
  Abstract Due to the uncertain nature of drought, it is one of the most menacing natural disasters. Drought modeling (Prediction, Detection, Forecasting, and Stage Prediction) is very essential for efficient policy making. But one of the key problems with droug…
  openalex Muhammad Owais Raza, Aqsa Umar, Jawad Rasheed, Tunç Aşuroğlu 等 2025-05-11 置信度 0.72 Transfer of learningStage (stratigraphy)Computer scienceTransfer (computing)Artificial intelligence
• RUL Prediction of Lithium-ion Batteries using a Federated and Homomorphically Encrypted Learning Method
  The increasing demand for lithium-ion batteries (LIB) across various industries has accentuated the importance of accurately predicting the Remaining Useful Life (RUL) of these energy storage devices. This article introduces a novel approach to RUL prediction …
  openalex Víctor López, Óscar Fontenla-Romero, Elena Hernández-Pereira, Bertha Guijarro‐Berdiñas 等 2024-04-08 置信度 0.72 Lithium (medication)IonComputer scienceMaterials scienceComputational science
• Federated LeViT-ResUNet for Scalable and Privacy-Preserving Agricultural Monitoring Using Drone and Internet of Things Data
  Precision agriculture is necessary for dealing with problems like pest outbreaks, a lack of water, and declining crop health. Manual inspections and broad-spectrum pesticide application are inefficient, time-consuming, and dangerous. New drone photography and …
  openalex Mohammad Aldossary, Jaber Almutairi, Ibrahim Alzamil 2025-04-10 置信度 0.72 DroneInternet of ThingsScalabilityComputer scienceInternet privacy
• Dual prompt personalized federated learning in foundation models
  Personalized federated learning (PFL) has garnered significant attention for its ability to address heterogeneous client data distributions while preserving data privacy. However, when local client data is limited, deep learning models often suffer from insuff…
  openalex Ying Chang, Xiaohu Shi, Zhao Xiaohui, Zhaohuang Chen 等 2025-07-31 置信度 0.72 Foundation (evidence)Computer scienceFederated learningDual (grammatical number)Data science
• Secure and Differentially Private Edge-Cloud Federated Learning Framework for Privacy-Preserving Maritime AIS Intelligence
  Cloud computing now supports large-scale maritime analytics, yet offloading rich Automatic Identification System (AIS) data to the cloud exposes sensitive operational patterns and complicates compliance with cross-border priv... | Find, read and cite all the r…
  openalex Abuzar Khan, Abid Iqbal, Ghassan Husnain, Fahad Masood 等 2026-01-01 置信度 0.72 Computer scienceCloud computingPayload (computing)Differential privacyHomomorphic encryption
• Design of an Immersive Basketball Tactical Training System Based on Digital Twins and Federated Learning
  To address the challenges of dynamic adversarial scenario modeling distortion, insufficient cross-institutional data privacy protection, and simplistic evaluation systems in collegiate basketball tactical education, this study proposes and validates an immersi…
  openalex Xiongce Lv, Ye Tao, Yifan Zhang, Yang Xue 2025-03-31 置信度 0.72 BasketballComputer scienceTraining (meteorology)MultimediaHuman–computer interaction
• Federated Incentive Learning: A Privacy-Preserving Framework for Ad Monetization and Creator Rewards in High-Concurrency Environments
  Growing regulatory pressures have upended the historical pattern of cross-site behavioral targeting and measurement, forcing ad monetization systems to reconcile personalization, creator incentives, and privacy by design. This paper introduces Federated Incent…
  openalex Xun Yi 2025-10-13 置信度 0.72 MonetizationIncentiveComputer scienceSandbox (software development)Earnings
• Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
  Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human--computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich col…
  openalex Shibo Zhang, Yaxuan Li, Shen Zhang, Farzad Shahabi 等 2021-10-31 置信度 0.72 Wearable computerComputer scienceDeep learningWearable technologyActivity recognition
• The role of explainable AI in enhancing breast cancer diagnosis using machine learning and deep learning models
  Breast cancer is still a big health issue around the world, and it needs to be found quickly and perfectly to improve patient outcomes and lower death rates. Although artificial intelligence (AI) has showed amazing promise in breast cancer prediction mainly ma…
  openalex Zulfikar Ali Ansari, Manish Madhava Tripathi, Rafeeq Ahmed 2025-05-26 置信度 0.72 Artificial intelligenceBreast cancerComputer scienceDeep learningCancer
• Ensuring Zero Trust in GDPR-Compliant Deep Federated Learning Architecture
  Deep Federated Learning (DFL) revolutionizes machine learning (ML) by enabling collaborative model training across diverse, decentralized data sources without direct data sharing, emphasizing user privacy and data sovereignty. Despite its potential, DFL’s appl…
  openalex Zahra Abbas, Sunila Fatima Ahmad, Adeel Anjum, Madiha Haider Syed 等 2025-08-04 置信度 0.72 ArchitectureZero (linguistics)Computer scienceComputer securityBusiness
• Federated Learning with Pareto Optimality for Resource Efficiency and Fast Model Convergence in Mobile Environments
  Federated learning (FL) is an emerging distributed learning technique through which models can be trained using the data collected by user devices in resource-constrained situations while protecting user privacy. However, FL has three main limitations: First, …
  openalex June-Pyo Jung, Young‐Bae Ko, Sung‐Hwa Lim 2024-04-12 置信度 0.72 Computer scienceCluster analysisConvergence (economics)Pareto principleScheme (mathematics)