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• 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 0.84 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 0.84 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 0.84 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 0.84 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 0.84 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 0.84 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 0.84 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 0.84 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 0.84 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 0.84 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 0.84 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 0.82 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 0.82 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 0.84 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 0.84 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 0.82 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 0.84 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 0.84 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 0.84 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 0.82 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 0.84 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 0.84 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 0.84 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 0.84 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 0.84 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 0.80 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 0.84 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 0.84 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 0.84 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 0.84 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 0.84 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 0.84 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 0.84 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 0.84 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 0.82 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 0.84 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 0.84 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 0.78 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 0.84 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 0.84 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 0.84 Computer scienceCluster analysisConvergence (economics)Pareto principleScheme (mathematics)
• Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling
  BACKGROUND: Artificial intelligence-enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains underexplored. METHODS: A convolutional neural network was …
  openalex Joshua Mayourian, William La Cava, Akhil Vaid, Girish N. Nadkarni 等 2024-02-05 置信度 0.84 MedicineReceiver operating characteristicLeft ventricular hypertrophyInternal medicineCardiology
• Towards Secure and Efficient Farming Using Self-Regulating Heterogeneous Federated Learning in Dynamic Network Conditions
  The advancement of precision agriculture increasingly depends on innovative technological solutions that optimize resource utilization and minimize environmental impact. This paper introduces a novel heterogeneous federated learning architecture specifically d…
  openalex Sai Puppala, Koushik Sinha 2025-04-25 置信度 0.84 AgricultureComputer scienceBusinessComputer networkBiology
• Practical Federated Learning without a Server
  Federated Learning (FL) enables end-user devices to collaboratively train ML models without sharing raw data, thereby preserving data privacy. In FL, a central parameter server coordinates the learning process by iteratively aggregating the trained models rece…
  openalex Akash Dhasade, Anne-Marie Kermarrec, Erick Lavoie, Johan Pouwelse 等 2025-03-30 置信度 0.82 Computer scienceWorld Wide Web
• Intelligent waste sorting for urban sustainability using deep learning
  Smart cities’ have experienced an increasingly higher rate of urbanization and increase of the population leading to strengthening the pressing needs in waste management. In this paper, we present an intelligent waste classification system that utilises Convol…
  openalex G.F. Ahmad, Fizza Muhammad Aleem, Tahir Alyas, Qaiser Abbas 等 2025-07-25 置信度 0.84 SustainabilitySortingComputer scienceUrban sustainabilityDeep learning
• AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learning
  Federated learning enables users to collaboratively train a machine learning model over their private datasets. Secure aggregation protocols are employed to mitigate information leakage about the local datasets from user-submitted model updates. This setup, ho…
  openalex Ghada Almashaqbeh, Zahra Ghodsi 2025-03-07 置信度 0.84 AnonymityInternet privacyComputer scienceComputer securityFederated learning
• Deep learning for intrusion detection in emerging technologies: a comprehensive survey and new perspectives
  Abstract Intrusion Detection Systems (IDS) can help cybersecurity analysts detect malicious activities in computational environments. Recently, Deep Learning (DL) methods in IDS have demonstrated notable performance, revealing new underlying cybersecurity patt…
  openalex Euclides Carlos Pinto Neto, Shahrear Iqbal, Scott Buffett, Madeena Sultana 等 2025-08-20 置信度 0.84 Computer scienceIntrusion detection systemDeep learningData scienceEmerging technologies
• Federated learning for the pathogenicity annotation of genetic variants in multi-site clinical settings
  MOTIVATION: Rare diseases collectively affect 5% of the population. However, fewer than 50% of rare disease patients receive a molecular diagnosis after whole genome sequencing. Supervised machine learning is a valuable approach for the pathogenicity scoring o…
  openalex Nigreisy Montalvo, Francisco Requena Silvente, Emidio Capriotti, Antonio Rausell 2025-09-19 置信度 0.84 Genetic variantsAnnotationPathogenicityComputer scienceLicense
• Blockchain-based federated learning framework for malicious node detection in internet of vehicles (IoV) networks using fog and cloud computing
  Due to the continuous digitalization, IoV networks are vulnerable to various communication attacks by malicious network nodes. In these attacks, the malicious entities disseminate faulty information in the network, which affects quick and intelligent decision-…
  openalex Srinivas Reddy Bandarapu, Muhammad Bilal, Pushpalika Chatterjee, Adnan Mustafa Cheema 等 2025-07-21 置信度 0.84 BlockchainCloud computingComputer scienceNode (physics)The Internet
• Realistic Urban Traffic Generator Using Decentralized Federated Learning for the SUMO Simulator
  Realistic urban traffic simulation is essential for sustainable urban planning and the development of intelligent transportation systems. However, generating high-fidelity, time-varying traffic profiles that accurately reflect real-world conditions, especially…
  openalex Alberto Bazán-Guillén, Carlos Beis-Penedo, Diego Cajaraville-Aboy, Pablo Barbecho Bautista 等 2025-01-01 置信度 0.84 Generator (circuit theory)Computer scienceSimulationTransport engineeringEngineering
• A review of security threats and privacy issues in federated learning
  openalex Abhishek Kumar Agrahari, Aarti Gautam Dinker, Rajendra Bahadur Singh 2026-03-02 置信度 0.73 Computer scienceAdversarial systemComputer securityBackdoorFederated learning
• Federated Learning with Adversarial Optimisation for Secure and Efficient 5G Edge Computing Networks
  With the evolution of 5G edge computing networks, privacy-aware applications are gaining significant attention due to their decentralised processing capabilities. However, these networks face substantial challenges to ensure privacy and security, specifically …
  openalex Saniya Zafar, Jonathan White, Phil Legg 2025-09-17 置信度 0.84 Adversarial systemComputer scienceAdversaryRobustness (evolution)Edge computing
• Federated Learning for Privacy-Preserving Apparel Supply Chain Analytics
  The apparel industry operates through highly complex and globalized supply chains, where effective data analytics plays a critical role in improving demand forecasting, inventory management, logistics coordination, and sustainability practices. However, organi…
  openalex Mizanur Rahman, Samsul Haque, S M Arif Al Sany 2025-10-16 置信度 0.84 Supply chainAnalyticsComputer scienceBig dataSustainability
• Federated Learning for Medical Image Analysis: Privacy-Preserving Paradigms and Clinical Challenges
  Federated Learning (FL) has emerged as a transformative paradigm in medical image analysis, addressing the critical challenges of data scarcity and patient privacy. By enabling collaborative model training across decentralized datasets without requiring data s…
  openalex Juntao Hu, Zhengjie Yang, Peng Wang, Guanyi Zhao 等 2025-08-11 置信度 0.84 Computer scienceImage (mathematics)Internet privacyData scienceArtificial intelligence
• Federated Learning for Early Cardiac Anomaly Prediction in Cross-Silo IoMT Environments
  Early detection of cardiovascular anomalies remains critical for proactive patient care, especially within the growing ecosystem of Internet of Medical Things (IoMT) devices. This study explores the application of Federated Learning (FL) to predict early cardi…
  openalex Michael Georgiades, Lakis Christodoulou, Andreas Chari, Kezhi Wang 等 2025-06-09 置信度 0.84 SiloComputer scienceAnomaly (physics)Anomaly detectionArtificial intelligence
• Federated Learning-Based Intrusion Detection Framework for Internet of Things and Edge Computing Backed Critical Infrastructure
  Modern Critical Infrastructure (CI) sectors including Smart Girds operate on the Internet of Things and edge computing paradigm. With the enormous growth of these sectors, there are emerging and escalating cyber threats. Traditional Machine Mearning (ML) appro…
  openalex Ruofei Meng, Awais Aziz Shah, Muhammad Ali Jamshed, Dimitrios P. Pezaros 2024-06-09 置信度 0.84 Computer scienceEdge computingInternet of ThingsCritical infrastructureEnhanced Data Rates for GSM Evolution
• An optimized oversampling-based federated transfer learning approach for rotating machinery cluster fault diagnosis
  Abstract With the development of distributed industrial systems, rotating machinery as the core power and transmission unit of complex distributed industrial systems, its fault diagnosis is very necessary and faces the serious challenge of Non-Independent and …
  openalex Zhao Xu, Liya Yu, Shaobo Li, Chuanjiang Li 等 2025-07-23 置信度 0.84 OversamplingFault (geology)Cluster (spacecraft)Transfer of learningComputer science
• AI-Driven Privacy Shield: A Secure and Privacy-Preserving Federated Learning Framework
  Emerging, highly skilled cyberattacks demand novel and robust techniques for AI-powered privacy preservation. Centralized machine learning models can be compromised with a single point of failure, a data breach, or an adversarial attack. The proposed work pres…
  openalex Yasir M. Abdal 2025-09-07 置信度 0.84 Computer scienceFederated learningAdversarial systemScalabilityEncryption
• Evaluating machine learning algorithms for energy consumption prediction in electric vehicles: A comparative study
  An accurate energy consumption prediction becomes crucial with increasing electric vehicle usage for effective power grid management. This research examined the performance of eleven machine learning models for this purpose: Ridge Regression, Lasso Regression,…
  openalex Izhar Hussain, Kok Boon Ching, Chessda Uttraphan, Kim Gaik Tay 等 2025-05-08 置信度 0.84 Computer scienceEnergy consumptionMachine learningEnergy (signal processing)Artificial intelligence
• Privacy-Preserving Generative AI in Healthcare Systems Using Federated Learning Approaches
  The research paper focuses on the mechanism of introducing Federated Learning alongside privacy-saving strategies in Generative Artificial Intelligence to healthcare applications. The study analyses the privacy protection/model accuracy trade-off by implementi…
  openalex Rajesh A. Poojari 2026-02-28 置信度 0.84 Federated learningComputer scienceDifferential privacyGenerative grammarArtificial intelligence
• SplitML: A Unified Privacy-Preserving Architecture for Federated Split-Learning in Heterogeneous Environments
  While Federated Learning (FL) and Split Learning (SL) aim to uphold data confidentiality by localized training, they remain susceptible to adversarial threats such as model poisoning and sophisticated inference attacks. To mitigate these vulnerabilities, we pr…
  openalex Devharsh Trivedi, Aymen Boudguiga, Nesrine Kaaniche, Nikos Triandopoulos 2026-01-07 置信度 0.84 Federated learningInferenceComputer scienceHomomorphic encryptionConfidentiality
• Toward intelligent and resilient microgrids: A survey of machine learning approaches for renewable energy integration
  openalex Darioush Razmi, Peyman Razmi, Oluleke Babayomi, Zhenbin Zhang 2026-04-24 置信度 0.73 Renewable energyComputer scienceArtificial intelligenceMachine learningEnergy (signal processing)
• FedCVG: a two-stage robust federated learning optimization algorithm
  Federated learning provides an effective solution to the data privacy issue in distributed machine learning. However, distributed federated learning systems are inherently susceptible to data poisoning attacks and data heterogeneity. Under conditions of high d…
  openalex Runze Zhang, Yang Zhang, Yating Zhao, Bin Jia 等 2025-05-26 置信度 0.84 Computer scienceStage (stratigraphy)Optimization algorithmAlgorithmArtificial intelligence
• A Survey on Federated Learning in Human Sensing
  Human Sensing, a field that leverages technology to monitor human activities, psycho-physiological states, and interactions with the environment, enhances our understanding of human behavior and drives the development of advanced services that improve overall …
  openalex Mohan Li, Martin Gjoreski, Pietro Barbiero, Gašper Slapničar 等 2025-01-07 置信度 0.84 Computer scienceData scienceHuman–computer interaction
• Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging
  Artificial Intelligence (AI) and deep learning models have revolutionized diagnosis, prognostication, and treatment planning by extracting complex patterns from medical images, enabling more accurate, personalized, and timely clinical decisions. Despite its pr…
  openalex Anh T. Tran, Tal Zeevi, Seyedmehdi Payabvash 2025-04-14 置信度 0.84 Generalizability theoryArtificial intelligenceComputer scienceNeuroimagingRobustness (evolution)
• A systematic literature review on federated cyber-attack detection for edge intelligence: Challenges, approaches, and future directions
  The rapid expansion of Edge Computing (EC) and Internet of Things devices has introduced significant cybersecurity challenges, necessitating advanced and privacy-preserving attack detection strategies. Traditional cyber-attack detection methods and centralized…
  openalex Zeseya Sharmin, Md Palash Uddin, Yong Xiang, Feifei Chen 2026-03-20 置信度 0.84 Computer scienceSystematic reviewEnhanced Data Rates for GSM EvolutionInformation retrievalData science
• Robust aggregation algorithms for federated learning in unreliable network environments
  Federated learning (FL) allows joint model training on distributed devices without losing data locality, but its results are significantly worse in unreliable network systems where packets are dropped, clients fail, resources are heterogeneous, and adversarial…
  openalex Ziyang Zeng, Shiyu Yang, Guanyu Ding 2025-10-30 置信度 0.84 Computer scienceNetwork packetOutlierOverhead (engineering)Resilience (materials science)
• Digital Twins: State of the art theory and practice, challenges, and open research questions
  Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation, optimisation and accurate forecasting. However, the theoretical framework and practical implementations o…
  openalex Angira Sharma, Edward Elson Kosasih, Jie Zhang, Alexandra Brintrup 等 2022-08-08 置信度 0.82 ImplementationComputer scienceField (mathematics)Key (lock)Data science
• Client to Server: Heterogeneous Distribution Knowledge Transfer for Federated Learning
  Federated learning (FL) is an emerging distributed machine learning paradigm that provides privacy guarantees for training robust models on distributed clients. The primary challenge of FL is data heterogeneity,which slows down model convergence and degrades m…
  openalex Rui Zhao, Xiao Yang, Peng Zhi, Zhihe Zhang 等 2025-08-05 置信度 0.84 Computer scienceDistribution (mathematics)Transfer (computing)Transfer of learningKnowledge transfer
• Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach
  Artificial intelligence (AI), and especially its sub-field of Machine Learning (ML), are impacting the daily lives of everyone with their ubiquitous applications. In recent years, AI researchers and practitioners have introduced principles and guidelines to bu…
  openalex Firas Bayram, Bestoun S. Ahmed 2024-12-18 置信度 0.84 Computer scienceTrustworthinessRobustness (evolution)Artificial intelligenceMachine learning
• DataSHIELD: mitigating disclosure risk in a multi-site federated analysis platform
  Motivation: The validity of epidemiologic findings can be increased using triangulation, i.e. comparison of findings across contexts, and by having sufficiently large amounts of relevant data to analyse. However, access to data is often constrained by practica…
  openalex Demetris Avraam, Rebecca Wilson, Noemi Aguirre Chan, Soumya Banerjee 等 2024-12-26 置信度 0.80 Computer scienceBusinessRisk analysis (engineering)
• A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction
  Intrusion detection systems are essential for securing wireless sensor networks (WSNs) and Internet of Things (IoT) environments against various threats. This study presents a novel hybrid machine learning (ML) model that integrates KMeans-SMOTE (KMS) for data…
  openalex Md. Alamin Talukder, Majdi Khalid, Nasrin Sultana 2025-02-07 置信度 0.84 Dimensionality reductionComputer scienceIntrusion detection systemReduction (mathematics)Machine learning
• Federated learning for privacy-preserving AI in human–robot collaboration for smart manufacturing
  Purpose The study aims to address privacy and security challenges in AI-driven human–robot collaboration (HRC) by developing a privacy-preserving federated learning framework. Traditional centralized AI models expose sensitive manufacturing data to cybersecuri…
  openalex Milad Rahmati 2025-04-21 置信度 0.84 Computer scienceRobotHuman–computer interactionHuman–robot interactionInternet privacy
• ZTID-IoV: Zero-Trust Intrusion Detection in IoV Using Neurosymbolic AI Approach With Federated Meta-Learning
  The rapid growth of the Internet of Vehicles (IoVs) and smart consumer electronics has generated cybersecurity concerns that require an intelligent, adaptable, and privacy-preserving Intrusion Detection System (IDS). This study introduces ZTID-IoV, a novel neu…
  openalex Farhan Ullah, Gautam Srivastava, Leonardo Mostarda, Umar Raza 2025-10-23 置信度 0.84 Computer scienceIntrusion detection systemCluster analysisTransparency (behavior)Transformer
• Using Homomorphic Proxy Re‐Encryption to Enhance Security and Privacy of Federated Learning‐Based Intelligent Connected Vehicles
  Intelligent connected vehicles (ICVs) are one of the fast‐growing directions that plays a significant role in the area of autonomous driving. To realize collaborative computation among ICVs, federated learning (FL) or federated‐based large language model (FedL…
  openalex Yang Bai, Y. S. Rao, Hongyan Wu, Juan Wang 等 2025-01-01 置信度 0.84 Homomorphic encryptionComputer scienceProxy (statistics)Computer securityProxy re-encryption
• Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
  This study presents a lightweight autoencoder-based approach for anomaly detection in digit recognition using federated learning on resource-constrained embedded devices. We implement and evaluate compact autoencoder models on the ESP32-CAM microcontroller, en…
  openalex Anja Tanović, Ivan Mezei 2025-07-30 置信度 0.84 AutoencoderComputer scienceMNIST databaseAnomaly detectionInference
• Breakthroughs in Brain Tumor Detection: Leveraging Deep Learning and Transfer Learning for MRI-Based Classification
  Identifying and classifying brain tumors play a pivotal role in gaining insights into their underlying mechanisms. In contemporary medical practice, the integration of Computer-assisted Diagnosis (CAD) and machine learning, particularly deep learning, has sign…
  openalex Alireza Golkarieh, Sajjad Rezvani Boroujeni, Kiana Kiashemshaki, Maryam Deldadehasl 等 2025-07-31 置信度 0.84 Transfer of learningDeep learningComputer scienceArtificial intelligenceMachine learning
• FedEmerge: An Entropy-Guided Federated Learning Method for Sensor Networks and Edge Intelligence
  Introduction: Federated Learning (FL) is a distributed machine learning paradigm where a global model is collaboratively trained across multiple decentralized clients without exchanging raw data. This is especially important in sensor networks and edge intelli…
  openalex Koffka Khan 2025-06-14 置信度 0.84 Computer scienceEnhanced Data Rates for GSM EvolutionEntropy (arrow of time)Artificial intelligenceData science
• RewardChain: A Blockchain-Based Incentive Mechanism for Federated Learning in Consumer-Centric Internet of Medical Things
  Federated learning is a promising approach that enables collaborative machine learning (ML) in distributed environments, such as the Internet of Medical Things (IoMT) while preserving consumer privacy. It allows multiple consumers to collaboratively train a mo…
  openalex Ahmad A Alsharidah, Devki Nandan Jha, Ellis Solaiman, Bo Wei 等 2025-10-27 置信度 0.84 Computer scienceIncentiveProcess (computing)Federated learningBlockchain
• A Multi-View-Based Federated Learning Approach for Intrusion Detection
  Intrusion detection aims to identify the unauthorized activities within computer networks or systems by classifying events into normal or abnormal categories. As modern scenarios often involve multi-source data, multi-view fusion deep learning methods are empl…
  openalex Jia Yu, Guoqiang Wang, Nianfeng Shi, Raghav Saxena 等 2025-10-24 置信度 0.84 Computer scienceIntrusion detection systemRobustness (evolution)AdaptabilityLeverage (statistics)
• Exploiting explanations for model extraction via knowledge distillation and mitigation with private counterfactuals.
  europepmc 2026 置信度 0.72
• The Social Connectedness Index: A large-scale dataset of social ties across geographic locations.
  europepmc 2026 置信度 0.68
• Neurovictimology and the risks of neurotechnologies and artificial intelligence: a forensic and legal perspective.
  europepmc 2026 置信度 0.68
• AI-driven optimization in cloud computing: a systematic review of cost, resource management, and security.
  Cloud computing environments face persistent structural challenges in cost control, dynamic resource allocation, and security risk management, which traditional infrastructure approaches fail to address adequately. This systematic literature review aimed to sy…
  europepmc 2026 置信度 0.94 computer scienceartificial intelligencecloud computingsystematic reviewsustainability
• "In ChatGPT-Powered Virtual Influencers We (Dis)Trust?": The Privacy Paradox and the Double-Edged Sword of Ubiquitous Large Language Model (LLM) Generative AI as a General Purpose Technology (GPT) in a Human-Centered AI Ecosystem.
  europepmc 2026 置信度 0.72
• Harnessing the power of FAIR data to advance sex/gender insights in psychotraumatology.
  europepmc 2026 置信度 0.68
• Research integrity and data ethics in AI-driven integrated healthcare: a critical appraisal.
  The convergence of artificial intelligence (AI) and healthcare is reshaping clinical practice, yet this transformation raises pressing questions about scientific rigor and ethical responsibility. This review provides a critical appraisal of research integrity …
  europepmc 2026 置信度 0.94 engineering ethicshealth caremedicinetransformative learningtransparency
• Machine learning-assisted highly efficient thermal management in function-oriented thermochromic smart windows.
  europepmc 2026 置信度 0.76
• Cross-modal privacy-preserving synthesis and mixture-of-experts ensemble for robust ASD prediction.
  europepmc 2025 置信度 0.76
• Chaotic cosine and logistic map based robust image encryption with dual-stage confusion-diffusion architecture.
  europepmc 2026 置信度 0.72
• Shadow AI in Swedish Health Care: Qualitative Analysis of Physicians' Free-Text Answers.
  europepmc 2026 置信度 0.68
• A privacy preserving synthetic learner dataset for learning analytics in technology enhanced higher education.
  europepmc 2026 置信度 0.76
• The pediatric AI readiness framework: bridging evidence to practice in pediatric artificial intelligence.
  europepmc 2026 置信度 0.68
• Limitations of current copyright frameworks for large language models trained on scientific literature.
  europepmc 2026 置信度 0.68
• AI Applications Integrating Legal and Regulatory Perspectives in Mental Health: Systematic Review.
  europepmc 2026 置信度 0.68
• Blockchain-Based Dynamic and Revocable Consent for Secondary Health Data Use: Systematic Review.
  Background The secondary use of health data holds substantial potential for advancing biomedical research, strengthening population health analytics, and enabling artificial intelligence-driven decision-making support. Yet, ensuring that such reuse respects pa…
  europepmc 2026 置信度 0.94 medicinehealth caredata sciencesystematic reviewbusiness
• Slow Harm After Dobbs v. Jackson Women's Health Organization: An Analytic Scoping Review of How Data and Surveillance Infrastructures Reshape Emergency Obstetric Care.
  europepmc 2026 置信度 0.68
• Editorial: Multimodal brain data integration and computational modeling.
  europepmc 2026 置信度 0.72
• SYNNER synthetic data generator framework.
  europepmc 2026 置信度 0.68
• The 2020 US Decennial Census is more private than you (might) think.
  europepmc 2025 置信度 0.68