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• DEx-FCL: Reproducibility package for adaptive Edge-IoT security management
  Reproducibility package update for the manuscript "DEx-FCL: Drift- and Explanation-Consistent Federated Continual Learning for Adaptive Edge-IoT Security Management." Version 1.0.1 adds the complete real-benchmark result CSVs and generated manuscript figures u…
  datacite P, Arul Selvam 2026 置信度 0.66
• Secure Federated Learning for High-Dimensional Healthcare Data Using Differential Privacy
  Machine learning algorithms may be trained on decentralized data using Federated Learning (FL) when sharing raw data is not possible owing to privacy concerns. One example of this kind of data is EHRs, or electronic health records, which store private informat…
  datacite Vishal Trivedi, Dr. Sunil Bhutoda 2025 置信度 0.66 Privacy Preservation, Federated Learning, Machine Learning, algorithms and architecture
• Secure Federated Learning for High-Dimensional Healthcare Data Using Differential Privacy
  Machine learning algorithms may be trained on decentralized data using Federated Learning (FL) when sharing raw data is not possible owing to privacy concerns. One example of this kind of data is EHRs, or electronic health records, which store private informat…
  datacite Vishal Trivedi, Dr. Sunil Bhutoda 2025 置信度 0.66 Privacy Preservation, Federated Learning, Machine Learning, algorithms and architecture
• ЭВОЛЮЦИЯ И ПЕРСПЕКТИВЫ ГЛУБОКОГО ОБУЧЕНИЯ: СОВРЕМЕННЫЕ ТЕНДЕНЦИИ И НАПРАВЛЕНИЯ РАЗВИТИЯ
  В данной статье рассматривается эволюция и современные тенденции развития глубокого обучения, одной из ключевых технологий искусственного интеллекта (ИИ). Анализируются исторические этапы развития глубоких нейронных сетей, начиная с первых моделей персептронов…
  datacite Ахмедов Бехруз Иброхим угли 2025 置信度 0.66
• ЭВОЛЮЦИЯ И ПЕРСПЕКТИВЫ ГЛУБОКОГО ОБУЧЕНИЯ: СОВРЕМЕННЫЕ ТЕНДЕНЦИИ И НАПРАВЛЕНИЯ РАЗВИТИЯ
  В данной статье рассматривается эволюция и современные тенденции развития глубокого обучения, одной из ключевых технологий искусственного интеллекта (ИИ). Анализируются исторические этапы развития глубоких нейронных сетей, начиная с первых моделей персептронов…
  datacite Ахмедов Бехруз Иброхим угли 2025 置信度 0.66
• SATP-FL: Lightweight Secure Aggregation via Trajectory Prediction in Vehicular Federated Learning
  Federated learning in vehicular networks faces critical challenges from high mobility, key agreement latency, and frequent secure aggregation failures. Existing protocols cannot effectively accommodate frequent vehicle departures, leading to severely constrain…
  datacite Xion, Peng, Ding, Chunfa 2026 置信度 0.66
• SATP-FL: Lightweight Secure Aggregation via Trajectory Prediction in Vehicular Federated Learning
  Federated learning in vehicular networks faces critical challenges from high mobility, key agreement latency, and frequent secure aggregation failures. Existing protocols cannot effectively accommodate frequent vehicle departures, leading to severely constrain…
  datacite Xion, Peng, Ding, Chunfa 2026 置信度 0.66
• Federated Foundation Models and Multi-Agent Orchestration: Enabling Autonomous Decision Intelligence for Enterprise AI Systems and Adaptive Governance
  The rapid maturation of large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent orchestration frameworks has catalyzed a new generation of autonomous decision-support systems capable of reasoning over heterogeneous enterprise data, …
  datacite Yuanyuan, Wu, Ruxing Wang, Lijuan Guo 2026 置信度 0.66 Large Language Models; Multi-Agent Systems; Retrieval-Augmented Generation; Federated Learning; Foundation Models; Enterprise AI; Autonomous Agents; AI Governance; Model Orchestration; Differential Privacy
• Federated Foundation Models and Multi-Agent Orchestration: Enabling Autonomous Decision Intelligence for Enterprise AI Systems and Adaptive Governance
  The rapid maturation of large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent orchestration frameworks has catalyzed a new generation of autonomous decision-support systems capable of reasoning over heterogeneous enterprise data, …
  datacite Yuanyuan, Wu, Ruxing Wang, Lijuan Guo 2026 置信度 0.66 Large Language Models; Multi-Agent Systems; Retrieval-Augmented Generation; Federated Learning; Foundation Models; Enterprise AI; Autonomous Agents; AI Governance; Model Orchestration; Differential Privacy
• Edge Intelligence and 5G Networks: Enabling Smart IoT Systems for Real-Time Automation and Adaptive Control
  The rapid convergence of edge computing, fifth-generation (5G) wireless networks, and the Internet of Things (IoT) has catalyzed a new generation of intelligent, distributed automation systems capable of processing vast volumes of sensor data at or near the so…
  datacite Yuanyuan, Wu, Ruxing Wang, Lijuan Guo 2025 置信度 0.66 Edge Computing; 5G Networks; Internet of Things; TinyML; Smart Automation; Federated Learning; Real-Time Control; Cyber-Physical Systems; Predictive Maintenance; Adaptive Scheduling
• Edge Intelligence and 5G Networks: Enabling Smart IoT Systems for Real-Time Automation and Adaptive Control
  The rapid convergence of edge computing, fifth-generation (5G) wireless networks, and the Internet of Things (IoT) has catalyzed a new generation of intelligent, distributed automation systems capable of processing vast volumes of sensor data at or near the so…
  datacite Yuanyuan, Wu, Ruxing Wang, Lijuan Guo 2025 置信度 0.66 Edge Computing; 5G Networks; Internet of Things; TinyML; Smart Automation; Federated Learning; Real-Time Control; Cyber-Physical Systems; Predictive Maintenance; Adaptive Scheduling
• [Data] Federated Learning for Self-Supervised Acoustic Intelligence in LPBF: Physics-Guided Learnable-QSincNet for Cross-Domain Representation with Causally Explainable Spectral Insights
  Airborne acoustic emission (AE) forms a rich component of Laser Powder Bed Fusion (LPBF) process emissions and offers a high-value channel for in-situ process monitoring; however, leveraging this information for industrial deployment remains challenging due to…
  datacite Pandiyan, Vigneashwara Pandiyan, Wrobel, Rafal, shevchik, sergey, Leinenbach, Christian 2025 置信度 0.66
• Foundation Models for Automated Radiology Report Generation in Oncology Imaging
  Radiology reports serve as the primary communication medium between radiologists and referring clinicians, providing critical information for diagnosis, treatment planning, and disease monitoring. In oncology imaging, the growing volume and complexity of radio…
  datacite Sneha Waghmare*1, Meera Nair2, Arjun Verma3, Shatrughna Nagrik4 2026 置信度 0.66 Foundation models; Radiology report generation; Oncology imaging; Large language models; Vision-language models; Artificial intelligence; Medical imaging
• Foundation Models for Automated Radiology Report Generation in Oncology Imaging
  Radiology reports serve as the primary communication medium between radiologists and referring clinicians, providing critical information for diagnosis, treatment planning, and disease monitoring. In oncology imaging, the growing volume and complexity of radio…
  datacite Sneha Waghmare*1, Meera Nair2, Arjun Verma3, Shatrughna Nagrik4 2026 置信度 0.66 Foundation models; Radiology report generation; Oncology imaging; Large language models; Vision-language models; Artificial intelligence; Medical imaging
• Υλοποίηση εφαρμογής ανίχνευσης κυβερνοεπιθέσεων σε ασύρματα δίκτυα 5G/6G με χρήση αποκεντρωμένων LLMs
  datacite Kontogiannis, Evangelos 2026 置信度 0.66 Ανίχνευση ανωμαλιώνΑρχεία καταγραφήςΟμοσπονδιακή μάθησηΜεγάλα γλωσσικά μοντέλαΚυβερνοασφάλεια
• Parallels and Interconnections Between Mathematics, Art, Writing, and Music: Living the Metaverse and Beyond
  From basic screen-based interfaces into completely immersive, multidimensional worlds, the metaverse marks a great evolutionary leap in digital human interaction. As fundamental pillars for creating and living within the metaverse and beyond, this study looks …
  datacite Mageed, Ismail A 2026 置信度 0.66 MetaverseInterdisciplinarityGraph TheoryDigital TwinsEmpathic Computing
• Parallels and Interconnections Between Mathematics, Art, Writing, and Music: Living the Metaverse and Beyond
  From basic screen-based interfaces into completely immersive, multidimensional worlds, the metaverse marks a great evolutionary leap in digital human interaction. As fundamental pillars for creating and living within the metaverse and beyond, this study looks …
  datacite Mageed, Ismail A 2026 置信度 0.66 MetaverseInterdisciplinarityGraph TheoryDigital TwinsEmpathic Computing
• Execution Governance 5.0 Research Architecture: From Governed Effect Fabrics to Governed Effect Regimes
  Execution Governance 5.0 (EG5) Research Architecture v0.1.4 proposes a candidate major-version research direction extending Execution Governance from authority-preserving Governed Effect Fabrics to the time-evolving Governed Effect Regime that determines how s…
  datacite KU, Ho Wa 2026 置信度 0.66 Execution GovernanceExecution Governance 5.0EG5Governed Effect Regimegoverned effects
• Execution Governance 5.0 Research Architecture: From Governed Effect Fabrics to Governed Effect Regimes
  Execution Governance 5.0 (EG5) Research Architecture v0.1.4 proposes a candidate major-version research direction extending Execution Governance from authority-preserving Governed Effect Fabrics to the time-evolving Governed Effect Regime that determines how s…
  datacite KU, Ho Wa 2026 置信度 0.66 Execution GovernanceExecution Governance 5.0EG5Governed Effect Regimegoverned effects
• Enhanced Privacy-Preserving Federated Class-Incremental Learning Using Attention-Based Dynamic Aggregation and Continual Learning
  Federated Class-Incremental Learning (FCIL) enables distributed clients to collaboratively learn new classes without sharing raw data, thereby ensuring data privacy. However, existing FCIL methods suffer from catastrophic forgetting, inefficient client aggrega…
  datacite G, Hari Krishna, Reddy, Dr. A. Anand 2026 置信度 0.66 Federated Learning; Class-Incremental Learning; Differential Privacy; Rényi Differential Privacy; Bayesian Differential Privacy
• Enhanced Privacy-Preserving Federated Class-Incremental Learning Using Attention-Based Dynamic Aggregation and Continual Learning
  Federated Class-Incremental Learning (FCIL) enables distributed clients to collaboratively learn new classes without sharing raw data, thereby ensuring data privacy. However, existing FCIL methods suffer from catastrophic forgetting, inefficient client aggrega…
  datacite G, Hari Krishna, Reddy, Dr. A. Anand 2026 置信度 0.66 Federated Learning; Class-Incremental Learning; Differential Privacy; Rényi Differential Privacy; Bayesian Differential Privacy
• Federated Fine-Tuning of DziriBERT for Algerian Dialect Sentiment Analysis
  This study examines federated learning for sentiment analysis in Algerian Arabic and code-switched social media content using transformer-based language models. A corpus of 13,260 user-generated comments collected via the X platform API with balanced positive …
  datacite SLIMANI, NASREDDINE, Bendjima, Mustapha 2026 置信度 0.66 DziriBERT, Federated Learning, FedAvg, FedProx, Algerian dialect, NLP, sentiment analysis
• Federated Fine-Tuning of DziriBERT for Algerian Dialect Sentiment Analysis
  This study examines federated learning for sentiment analysis in Algerian Arabic and code-switched social media content using transformer-based language models. A corpus of 13,260 user-generated comments collected via the X platform API with balanced positive …
  datacite SLIMANI, NASREDDINE, Bendjima, Mustapha 2026 置信度 0.66 DziriBERT, Federated Learning, FedAvg, FedProx, Algerian dialect, NLP, sentiment analysis
• Dual asymmetric momentum improves federated class unlearning in edge systems (Code and Reproducibility Scripts)
  This Zenodo record provides the reference implementation of FedDAM, a parameter-efficient post-hoc federated class unlearning method designed for resource-constrained edge deployments. The release includes training and unlearning pipelines for CIFAR-10 and CIF…
  datacite Mayaluri, Zefree Lazarus, PATRA, ACHIRANGSHU, Sahoo, Prabodh, Kumawat, Gaurav 2026 置信度 0.66 federated learningfederated unlearningmachine unlearningclass unlearningedge AI
• Dual asymmetric momentum improves federated class unlearning in edge systems (Code and Reproducibility Scripts)
  This Zenodo record provides the reference implementation of FedDAM, a parameter-efficient post-hoc federated class unlearning method designed for resource-constrained edge deployments. The release includes training and unlearning pipelines for CIFAR-10 and CIF…
  datacite Mayaluri, Zefree Lazarus, PATRA, ACHIRANGSHU, Sahoo, Prabodh, Kumawat, Gaurav 2026 置信度 0.66 federated learningfederated unlearningmachine unlearningclass unlearningedge AI
• Autonomous IIoT Intrusion Detection via Federated Learning with Neural Thompson Sampling
  datacite Anonyme 2026 置信度 0.66
• Autonomous IIoT Intrusion Detection via Federated Learning with Neural Thompson Sampling
  datacite Anonyme 2026 置信度 0.66
• DEx-FCL: Reproducibility package for adaptive Edge-IoT security management
  Reproducibility package for the manuscript "DEx-FCL: Drift- and Explanation-Consistent Federated Continual Learning for Adaptive Edge-IoT Security Management." This release includes the DEx-FCL source code, deterministic federated partitioning utilities, fixed…
  datacite P, Arul Selvam 2026 置信度 0.66
• BrainPredict™ Breakthrough Innovations v2.0 — P1–P16 (Claims R31–R62)
  Complete source code, implementation plans, and technical documentation for 16 world-first AI innovations constituting the BrainPredict™ Breakthrough Innovations portfolio (P1–P16, Claims R31–R62), authored by Raphaël Clairin, April 1, 2026.\n\nThis deposit es…
  datacite Clairin, Raphael Pierre Eugene 2026 置信度 0.66 "enterprise AI", "on-premise AI", "EU AI Act", "compliance", "post-quantum cryptography", "federated learning", "IEC 61131-3", "PLC code generation", "zero-trust AI", "causal AI", "constitutional AI", "medical AI", "multi-modal AI", "blockchain timestamping", "BrainPredict", "FEAC", "ZTAF", "CDAE", "MAOS", "BrainScore"
• BrainPredict™ Breakthrough Innovations v2.0 — P1–P16 (Claims R31–R62)
  Complete source code, implementation plans, and technical documentation for 16 world-first AI innovations constituting the BrainPredict™ Breakthrough Innovations portfolio (P1–P16, Claims R31–R62), authored by Raphaël Clairin, April 1, 2026.\n\nThis deposit es…
  datacite Clairin, Raphael Pierre Eugene 2026 置信度 0.66 "enterprise AI", "on-premise AI", "EU AI Act", "compliance", "post-quantum cryptography", "federated learning", "IEC 61131-3", "PLC code generation", "zero-trust AI", "causal AI", "constitutional AI", "medical AI", "multi-modal AI", "blockchain timestamping", "BrainPredict", "FEAC", "ZTAF", "CDAE", "MAOS", "BrainScore"
• Evaluación de Mecanismos Semiasíncronos Deterministas y Adaptativos en Aprendizaje Federado
  El aprendizaje automático ha evolucionado significativamente en los últimos años, expandiendo su aplicación a entornos de Internet de las Cosas (IoT), donde estas técnicas poseen un gran potencial en sectores como la mejora de diagnósticos médicos, el reconoci…
  datacite Hidalgo Izquierdo, Víctor, Caminero, Maria Blanca, Carrión Espinosa, María del Carmen 2026 置信度 0.66 federated learningfog computinginternet of thingsmachine learningsemi-asynchrony
• Evaluación de Mecanismos Semiasíncronos Deterministas y Adaptativos en Aprendizaje Federado
  El aprendizaje automático ha evolucionado significativamente en los últimos años, expandiendo su aplicación a entornos de Internet de las Cosas (IoT), donde estas técnicas poseen un gran potencial en sectores como la mejora de diagnósticos médicos, el reconoci…
  datacite Hidalgo Izquierdo, Víctor, Caminero, Maria Blanca, Carrión Espinosa, María del Carmen 2026 置信度 0.66 federated learningfog computinginternet of thingsmachine learningsemi-asynchrony
• D11.1 - SIESTA risk control tools
  The objective of this report is to provide a brief summary of the activities carried out within the framework of SIESTA’s Work Package 11 (WP11) including the selection of developed or adopted tools, and interaction with other work packages, particularly those…
  datacite Rodriguez Gonzalez, David, Sáinz-Pardo Díaz, Judith, Tran, Viet 2026 置信度 0.66
• D11.1 - SIESTA risk control tools
  The objective of this report is to provide a brief summary of the activities carried out within the framework of SIESTA’s Work Package 11 (WP11) including the selection of developed or adopted tools, and interaction with other work packages, particularly those…
  datacite Rodriguez Gonzalez, David, Sáinz-Pardo Díaz, Judith, Tran, Viet 2026 置信度 0.66
• FairFed-Health: Deficit-Weighted Federated Aggregation for Institutional Equity in Privacy-Preserving ICU Mortality Prediction
  No description provided.
  datacite Gnanavel, Agash 2026 置信度 0.66 Federated LearningAlgorithmic FairnessEqualised OddsICU Mortality PredictionInstitutional Equity
• FairFed-Health: Deficit-Weighted Federated Aggregation for Institutional Equity in Privacy-Preserving ICU Mortality Prediction
  No description provided.
  datacite Gnanavel, Agash 2026 置信度 0.66 Federated LearningAlgorithmic FairnessEqualised OddsICU Mortality PredictionInstitutional Equity
• MS-FL: A FEDERATED LEARNING FRAMEWORK BASED ON MULTIPLE SECURITY STRATEGIES
  datacite IJERST 2026 置信度 0.66
• MS-FL: A FEDERATED LEARNING FRAMEWORK BASED ON MULTIPLE SECURITY STRATEGIES
  datacite IJERST 2026 置信度 0.66
• SCALABLE SMART CITY PLATFORM USING IOT AND CLOUD COMPUTING
  Rapid urbanization has significantly increased pressure on transportation systems, healthcare, energy distribution, environmental monitoring, waste management, public safety, and other municipal services. Traditional city management approaches are increasingly…
  datacite 1*Mustapha Malami Idina, 2Abubakar Jibo Magayaki, 3Mubarak Jibril Yeldu 2026 置信度 0.66
• SCALABLE SMART CITY PLATFORM USING IOT AND CLOUD COMPUTING
  Rapid urbanization has significantly increased pressure on transportation systems, healthcare, energy distribution, environmental monitoring, waste management, public safety, and other municipal services. Traditional city management approaches are increasingly…
  datacite 1*Mustapha Malami Idina, 2Abubakar Jibo Magayaki, 3Mubarak Jibril Yeldu 2026 置信度 0.66
• A DECENTRALIZED MACHINE LEARNING APPROACH FOR FRAUD DETECTION WITH BLOCKCHAIN-DRIVEN PRIVACY PROTECTION
  Modern digital ecosystems have the significant difficulty of detecting fraud while managing large, real-time data transfer and privacy preservation. This study presents an innovative architecture that combines blockchain technology with machine learning to pro…
  datacite KOPPULA, ANITHA, M, VENKATA NARASAIAH, Dr, CHAVA HARI BABU, DR, VUNNAVA DINESH BABU 等 2026 置信度 0.66 Blockchain, Fraud Detection, Smart Contracts, Data Confidentiality, Artificial Intelligence, Cybersecurity, Trust Management, Secure Data Sharing.
• A DECENTRALIZED MACHINE LEARNING APPROACH FOR FRAUD DETECTION WITH BLOCKCHAIN-DRIVEN PRIVACY PROTECTION
  Modern digital ecosystems have the significant difficulty of detecting fraud while managing large, real-time data transfer and privacy preservation. This study presents an innovative architecture that combines blockchain technology with machine learning to pro…
  datacite KOPPULA, ANITHA, M, VENKATA NARASAIAH, Dr, CHAVA HARI BABU, DR, VUNNAVA DINESH BABU 等 2026 置信度 0.66 Blockchain, Fraud Detection, Smart Contracts, Data Confidentiality, Artificial Intelligence, Cybersecurity, Trust Management, Secure Data Sharing.
• Privacy-Preserving Digital Twin Technologies for Smart City Infrastructure: Balancing Utility and Data Protection
  Smart city initiatives increasingly rely on digital twin technology to create comprehensive virtual representations of urban infrastructure. However, the extensive data collection raises significant privacy concerns as aggregated data can reveal sensitive info…
  datacite Safa Mohamed, Safa Kamal 2021 置信度 0.66 Machine LearningArtificial intelligence
• Privacy-Preserving Digital Twin Technologies for Smart City Infrastructure: Balancing Utility and Data Protection
  Smart city initiatives increasingly rely on digital twin technology to create comprehensive virtual representations of urban infrastructure. However, the extensive data collection raises significant privacy concerns as aggregated data can reveal sensitive info…
  datacite Safa Mohamed, Safa Kamal 2021 置信度 0.66 Machine LearningArtificial intelligence
• Code and trained models for "SNR-Conditioned Residual CNN with Feature-wise Linear Modulation for OFDM Channel Estimation: Ablation Analysis and Federated Deployment
  Reproducibility code, trained model weights, and result files for the paper "SNR-Conditioned Residual CNN with Feature-wise Linear Modulation for OFDM Channel Estimation: Ablation Analysis and Federated Deployment." Includes a self-contained PyTorch implementa…
  datacite Abdulhameed, Zainab, Hatem, Haraa, shehab, jinan 2026 置信度 0.66 OFDM · channel estimation · deep learning · feature-wise linear modulation · convolutional neural network · federated learning · LMMSE · pilot-aided estimation
• Code and trained models for "SNR-Conditioned Residual CNN with Feature-wise Linear Modulation for OFDM Channel Estimation: Ablation Analysis and Federated Deployment
  Reproducibility code, trained model weights, and result files for the paper "SNR-Conditioned Residual CNN with Feature-wise Linear Modulation for OFDM Channel Estimation: Ablation Analysis and Federated Deployment." Includes a self-contained PyTorch implementa…
  datacite Abdulhameed, Zainab, Hatem, Haraa, shehab, jinan 2026 置信度 0.66 OFDM · channel estimation · deep learning · feature-wise linear modulation · convolutional neural network · federated learning · LMMSE · pilot-aided estimation
• nimaomid/Context-Aware-Adaptive-Federated-Learning: CAFL v1.0.1
  Official implementation of the CAFL framework for adaptive federated learning in dynamic IoT environments.
  datacite nimaomid 2026 置信度 0.66
• nimaomid/Context-Aware-Adaptive-Federated-Learning: CAFL v1.0.1
  Official implementation of the CAFL framework for adaptive federated learning in dynamic IoT environments.
  datacite nimaomid 2026 置信度 0.66
• ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPROACHES FOR NEXT-GENERATION COMPUTING SYSTEMS
  Next-generation computing systems face unprecedented challenges in terms of scale, complexity, energy efficiency, and adaptability. Classical algorithmic approaches are increasingly insufficient to manage the demands imposed by exascale high-performance comput…
  datacite GINJALA SHIVA 2026 置信度 0.66
• ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPROACHES FOR NEXT-GENERATION COMPUTING SYSTEMS
  Next-generation computing systems face unprecedented challenges in terms of scale, complexity, energy efficiency, and adaptability. Classical algorithmic approaches are increasingly insufficient to manage the demands imposed by exascale high-performance comput…
  datacite GINJALA SHIVA 2026 置信度 0.66
• Decentralized Federated Learning with Differential Privacy via Homomorphic Encryption
  This paper presents a novel approach to decentralized federated learning that leverages the strengths of differential privacy and homomorphic encryption to achieve robust privacy guarantees. The core idea is to encrypt each participant's local data using homom…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Decentralized Federated Learning with Differential Privacy via Homomorphic Encryption
  This paper presents a novel approach to decentralized federated learning that leverages the strengths of differential privacy and homomorphic encryption to achieve robust privacy guarantees. The core idea is to encrypt each participant's local data using homom…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Distributed Generative Adversarial Networks with Federated Learning
  This paper presents a novel approach to generative modeling by integrating Generative Adversarial Networks (GANs) with Federated Learning (FL). Traditional GAN training suffers from centralized data requirements and privacy concerns. This research addresses th…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Distributed Generative Adversarial Networks with Federated Learning
  This paper presents a novel approach to generative modeling by integrating Generative Adversarial Networks (GANs) with Federated Learning (FL). Traditional GAN training suffers from centralized data requirements and privacy concerns. This research addresses th…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Shadow AI in Organizations
  This independent research report examines Shadow AI in organizations, defined as the informal or unauthorized use, deployment, fine tuning, or integration of generative AI tools outside established governance, security, privacy, and compliance processes. The s…
  datacite van Hamond, Johannes Maria 2026 置信度 0.66
• Shadow AI in Organizations
  This independent research report examines Shadow AI in organizations, defined as the informal or unauthorized use, deployment, fine tuning, or integration of generative AI tools outside established governance, security, privacy, and compliance processes. The s…
  datacite van Hamond, Johannes Maria 2026 置信度 0.66
• Differential Privacy for Federated Learning with Personalized Noise
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data, preserving data privacy. However, traditional differential privacy (DP) mechanisms often introduce significant noise into the global model updates, l…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Differential Privacy for Federated Learning with Personalized Noise
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data, preserving data privacy. However, traditional differential privacy (DP) mechanisms often introduce significant noise into the global model updates, l…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Distributed Federated Learning with Byzantine Fault Tolerance via Blockchain Verification
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, FL systems are vulnerable to Byzantine attacks, where malicious participants can inject faulty mode…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Distributed Federated Learning with Byzantine Fault Tolerance via Blockchain Verification
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, FL systems are vulnerable to Byzantine attacks, where malicious participants can inject faulty mode…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Decentralized Federated Learning with Byzantine Fault Tolerance via Consensus-Based Proofs
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants c…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Decentralized Federated Learning with Byzantine Fault Tolerance via Consensus-Based Proofs
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants c…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Information Bottleneck with Differential Privacy for Federated Learning
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing, thus addressing privacy concerns. However, FL is still susceptible to privacy breaches and suffers from significa…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Information Bottleneck with Differential Privacy for Federated Learning
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing, thus addressing privacy concerns. However, FL is still susceptible to privacy breaches and suffers from significa…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Distributed Graph Learning via Federated Bayesian Networks
  This paper presents a novel approach to distributed graph learning utilizing Federated Bayesian Networks (FBNs). The core challenge in training large graph neural networks (GNNs) lies in the substantial computational resources required, often necessitating cen…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Distributed Graph Learning via Federated Bayesian Networks
  This paper presents a novel approach to distributed graph learning utilizing Federated Bayesian Networks (FBNs). The core challenge in training large graph neural networks (GNNs) lies in the substantial computational resources required, often necessitating cen…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Distributed Federated Learning with Differential Privacy and Byzantine Fault Tolerance
  Federated Learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, existing FL frameworks are susceptible to various vulnerabilities, including privacy breaches thro…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Distributed Federated Learning with Differential Privacy and Byzantine Fault Tolerance
  Federated Learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, existing FL frameworks are susceptible to various vulnerabilities, including privacy breaches thro…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Federated Learning for Privacy-Preserving Data Analysis
  This paper explores the application of federated learning (FL) as a novel approach to privacy-preserving data analysis. Traditional data analysis methods often require centralized data collection, raising significant privacy concerns. Federated learning offers…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Federated Learning for Privacy-Preserving Data Analysis
  This paper explores the application of federated learning (FL) as a novel approach to privacy-preserving data analysis. Traditional data analysis methods often require centralized data collection, raising significant privacy concerns. Federated learning offers…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Privacy-preserving multicenter differential protein abundance analysis with FedProt
  Quantitative mass spectrometry has revolutionized proteomics by enabling simultaneous quantification of thousands of proteins. Pooling patient-derived data from multiple institutions enhances statistical power but raises serious privacy concerns. Here we intro…
  datacite Burankova, Yuliya, Abele, Miriam, Bakhtiari, Mohammad, Toerne, Christine von 等 2025 置信度 0.66
• Advances in Remote Monitoring Technology Applications in Anesthesia: A Narrative Review.
  The swift progress of digital and sensor technologies is hastening the incorporation of remote monitoring into anesthesiology. While several reviews have explored telemedicine and artificial intelligence in anesthesia, most existing summaries either focus on c…
  pubmed Chen L, Cheng S, Zhang L, Li H 等 2026 Jul 1 置信度 0.82
• F-BEGAN: BEGAN-enabled federated learning for imbalance data security and privacy-preserving of IoMT-based Healthcare 5.0.
  The advancement of cutting-edge technologies such as the Internet of Things (IoT) and Deep Learning (DL) has transformed the Internet of Medical Things (IoMT) based healthcare into a new paradigm known as the Healthcare 5.0. This paradigm shift, particularly w…
  pubmed Ullah Z, Jiang W, Gharawi AA, Alahmadi MD 2026 Jun 30 置信度 0.82
• An AI-enabled federated blockchain framework for adaptive energy coordination in smart electric mobility networks.
  The high rate of electric vehicles (EVs) development has motivated the issues of peak load congestion, data privacy, scalability, and secure energy coordination in smart electric mobility networks. The traditional centralized EV charging management systems hav…
  pubmed Alghamdi TA, Almalki SA 2026 Jul 1 置信度 0.82
• Deep learning in tumour genomics: from multi-omics integration to precision oncology.
  Cancer remains a leading cause of death globally, with nearly 10 million deaths in 2020. Advances in genomic technologies have revolutionized cancer research, shifting focus towards precision medicine based on comprehensive tumour genomic profiling. Concurrent…
  pubmed Zhou Z, Li Z, Wang S, Ang MY 等 2026 置信度 0.82
• FedFound: a federated foundation model for lifespan brain morphological connectome analysis.
  The brain morphological connectome derived from structural MRI reflects inter-regional morphological relationships, providing a powerful representation for characterizing individual variability and detecting abnormalities across the lifespan. However, these ab…
  pubmed Han K, Hu D, Wang Y, Wu Z 等 2026 Jun 30 置信度 0.82
• Somatic variant-calling beyond cancer: Repurposing algorithms to map low‑allele‑fraction variants across genomics.
  Somatic variant callers were originally developed to identify tumour-specific mutations in mixed tumour-normal samples. Increasingly, disciplines such as developmental biology, reproductive medicine, virology and mitochondrial genetics require detection of low…
  pubmed Taylor N, Hearn TJ 2026 Jun 29 置信度 0.82
• AI-Augmented Hematological Signatures for Equitable Detection of Hereditary Hemolytic Anemia Carriers: A Global Systematic Review and Meta-Analysis.
  Artificial intelligence (AI) augmentation of routine hematological tests offers a promising strategy to improve hereditary hemolytic anemia (HHA) carrier detection in premarital screening, especially in resource-limited settings. HHA in this review specificall…
  pubmed Ali NT, Abdullah RS, Mehdi MAH, Ali GS 等 2026 置信度 0.82
• Navigating AI and machine learning in cancer research: an end-to-end translational framework.
  Cancer is a complex and heterogeneous disease that is characterized by multi-level biological variability. Advances in high-throughput technologies have led to large-scale, high-dimensional data sets in cancer research, creating a pressing need for powerful co…
  pubmed Saha S, Ali MS, Tengli AK, Prasad SR 等 2026 Jun 27 置信度 0.82
• Understanding the Barriers to Translating Artificial Intelligence into the Clinical Laboratory.
  Artificial intelligence (AI) is having a transformational impact on society, yet its adoption in laboratory medicine has proceeded notably slower than in many other industries and even different specialities within medicine. This review sets out to examine why…
  pubmed Neale M, Wong C, Kreuter D, Taylor J 等 2026 置信度 0.82
• Analysis of artificial intelligence errors in practical medicine and rehabilitation.
  Aim: To structure the types of errors that occur when using artificial intelligence in healthcare, as well as assess their impact on the accuracy of diagnostics and therapeutic decisions. Identify ways to minimize errors and increase the effectiveness of the u…
  pubmed Mintser OP, Hanynets PP, Sarcanich OV 2026 置信度 0.82
• FedCARE: Fuzzy-Supervised Federated Inference with Confidence Gating for Resilient IIoT Sensor Networks.
  Safety-critical Industrial Internet of Things (IIoT) sensor networks deployed in disaster scenarios require intelligent routing mechanisms that prioritize mission-critical packets without relying on centralized coordination. Federated learning on resource-cons…
  pubmed Mostafa B, Haj Ahmad H, Rabaiah Y, Elseddik M 2026 置信度 0.82
• Privacy-Preserving Federated Deep Learning for Robust Anomaly Detection in Distributed Security Sensing Systems.
  With the widespread adoption of intelligent terminals, edge devices, and distributed information systems in the financial domain, financial security sensing data exhibit multisource heterogeneity, dynamic temporal patterns, and high privacy sensitivity. Tradit…
  pubmed Xu D, Chen H, Zeng Y, Yang Y 等 2026 Jun 19 置信度 0.82
• DynamicFU: Contribution-Aware Dynamic Federated Unlearning for Industrial IoT.
  The Industrial Internet of Things (IIoT) increasingly relies on federated learning (FL) to enable collaborative model training without directly sharing raw traffic data across industrial sites. However, in practical IIoT deployments, clients may later request …
  pubmed Wu Z, He B, Si Z, Liao X 等 2026 置信度 0.82
• Energy-Efficient Cryptographic Protocols for Sustainable IoT Security: A Federated Learning-Enhanced Lightweight Framework with Post-Quantum Resilience.
  The increasing pace of Internet of Things (IoT) and Industrial Internet of Things (IIoT) applications has exacerbated the security challenges in resource-constrained environments, where traditional cryptographic protocols incur prohibitively high computational…
  pubmed Alshammari A 2026 Jun 8 置信度 0.82
• Harnessing Machine Learning for Accelerated Drug Discovery: Opportunities and Unmet Challenges.
  The process of drug discovery is one of the most expensive, time-consuming, and high-risk endeavors in modern science. Translating initial scientific insights into safe and effective therapies, supported by genomics, structural biology, and computational chemi…
  pubmed El-Tanani M, Rabbani SA, Wali AF, Muhana F 等 2026 May 22 置信度 0.82
• The Unfinished Ecosystem: Why Remote Patient Monitoring Has Matured Unevenly, and What Closing the Gap Will Require.
  Remote patient monitoring (RPM) is widely framed as a foundational technology for the next generation of chronic-disease care. Specific applications-pacemaker follow-up, hypertension cohorts, structured heart-failure programmes, post-surgical biosensor protoco…
  pubmed Ajagbe TS 2026 置信度 0.82
• FedVI: Financial Cross-Domain Federated Learning with Scarce Overlapping Samples via Visual Representation of Heterogeneous Tabular Data and Meta-Optimization.
  Federated learning offers a promising approach for cross-institutional financial risk control modeling but encounters two key challenges in practice: feature space heterogeneity and low sample overlap rate. Current federated transfer learning methods often rel…
  pubmed Yuan K, Wu J 2026 置信度 0.82
• Current State and Future of Artificial Intelligence in Pediatric Interventional Radiology: A Narrative Review.
  Artificial intelligence (AI) is reshaping the field of diagnostic radiology; however, its applications in interventional radiology and pediatric interventional radiology (PIR) remain limited despite clear clinical needs and the rich multimodal data environment…
  pubmed Al-Sharydah AM 2026 Jun 20 置信度 0.82
• Edge-AI enabled secure IoT framework for real-time patient monitoring and anomaly detection in smart healthcare systems.
  Hospitals desire knowledge of bedside sensors in real time but they do not wish to send everything to the cloud. We propose an Edge-AI framework used in the IoT environment that maintains intelligence as near as possible to the patient, and ensures privacy and…
  pubmed Karpagam P, Karthikeyan M, Kalpana G, Suresh A 2026 置信度 0.82
• Mechanistic Insights into the Role of Artificial Intelligence and Machine Learning in the Diagnosis and Management of Multiple Sclerosis.
  Multiple sclerosis (MS) is a chronic, immune-mediated demyelinating disease of the central nervous system whose heterogeneous clinical, radiological, and biological course has long resisted precise individual-level prediction. The recent convergence of large l…
  pubmed Minagar A, Sahraian M 2026 May 27 置信度 0.82
• Radiomics in Medical Imaging: Methods, Applications, and Challenges.
  Radiomics enables quantitative medical image analysis by converting imaging data into structured, high-dimensional feature representations for predictive modeling. Despite methodological developments and encouraging retrospective results, radiomics continue to…
  pubmed Neha F, Shukla DK 2026 May 23 置信度 0.82
• Bridging Ancestry-Stratified Bias in Pharmacogenomics AI: Toward Metabolomics-Inclusive Multi-Omics Precision Medicine.
  Pharmacogenomics AI offers significant potential for individualized drug therapy; however, its clinical benefits remain unevenly distributed. Models trained predominantly on European-ancestry data consistently underperform in non-European populations, with pol…
  pubmed Lee H, Sajid K, Lee D 2026 Jun 20 置信度 0.82
• Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care.
  Rare diseases affect over 300 million individuals worldwide yet remain underdiagnosed and poorly characterized due to fragmented data, small cohorts, and phenotypic heterogeneity. Advances in artificial intelligence (AI) are enabling integration of genomics, i…
  pubmed Thongprayoon C, Pesce F, Cheungpasitporn W 2026 置信度 0.82
• [Digital transformation in multiple sclerosis: Advances in diagnostics, monitoring and patient-centred care].
  Digital transformation is fundamentally changing the diagnosis, monitoring and treatment of multiple sclerosis. The integration of multimodal data from imaging, laboratory tests, clinical assessments, patient-reported outcomes and continuous measurements via w…
  pubmed Voigt I, Inojosa H, Pawlitzki M, Masanneck L 等 2026 Jun 24 置信度 0.82
• Privacy-aware diabetic retinopathy grading and visual lesion-focused interpretability through mixture-of-experts federated deep learning with explainable AI.
  Diabetic Retinopathy (DR) is still a major cause of vision loss that can be avoided. This means that we need automated screening systems that can work across institutions without putting sensitive medical data in one place. Although Federated Learning (FL) all…
  pubmed Tashrif MTA, Kundu D, Bithee MMA, Rahman A 等 2026 Jun 23 置信度 0.82
• TTP-SSFL: Test-Time Personalization Self-Supervised Federated Learning for Accelerating MR Image Reconstruction.
  Federated learning (FL) has emerged as a promising paradigm for accelerating magnetic resonance (MR) image reconstruction while preserving data privacy in multicenter collaborations. However, existing FL-based reconstruction methods face two major challenges: …
  pubmed Geng C, Jiang M, Ruan D, Yu C 等 2026 Jun 23 置信度 0.82
• Spatio-temporal graph ConvLSTM with hierarchical personalized federated learning for cooperative zero-day intrusion detection in 6G internet of vehicles.
  In growing 6G-enabled Internet of Vehicles (IoV) environments, in-car networks (IVNs) are more susceptible to sophisticated cyber threats, especially zero-day attacks that avoid signature-based detection. The centralised data dependency, lack of geographical a…
  pubmed Alghamdi M, Algahtani MA, Abouelkheir E, Ibraheem A 等 2026 Jun 22 置信度 0.82
• Integrating experimental biology, computational methods, and artificial intelligence in anticancer drug discovery: Bridging the translational gap.
  Cancer remains a leading cause of mortality globally, with the incidence projected to reach 28.4 million new cases annually by 2040. Traditional drug discovery is notoriously inefficient: 10-17 years and up to $2.8 billion per approved drug, with fewer than 10…
  pubmed Zemnou CT, Ngakam R, Tepap SSD, Simo FBN 等 2026 Aug 15 置信度 0.82
• FedLASE: Performance-balanced system-heterogeneous FL via layer-adaptive submodel extraction.
  Federated Learning (FL) has gained significant attention for its privacy-preserving capabilities in distributed learning environments. However, the inherent system heterogeneity across edge devices brings significant challenges in deploying a unified global mo…
  pubmed Hu Q, Liao T, Wu S, Zheng Z 等 2026 Jun 15 置信度 0.82