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• FedBuild: Privacy-Preserving Federated Learning for Building Energy Forecasting
  FedBuild implements a production-grade federated learning system with formal differential privacy (DP) guarantees. The system trains CNN-LSTM models across 50 buildings using the Flower framework, applies per-sample gradient clipping and Gaussian noise via Opa…
  datacite Ameh, Jude 2026 置信度 0.66 federated learningdifferential privacydp-sgdprivacy-preserving machine learningbuilding energy forecasting
• FedBuild: Privacy-Preserving Federated Learning for Building Energy Forecasting
  FedBuild implements a production-grade federated learning system with formal differential privacy (DP) guarantees. The system trains CNN-LSTM models across 50 buildings using the Flower framework, applies per-sample gradient clipping and Gaussian noise via Opa…
  datacite Ameh, Jude 2026 置信度 0.66 federated learningdifferential privacydp-sgdprivacy-preserving machine learningbuilding energy forecasting
• Streaming Federated Learning with Markovian Data
  datacite Huynh, Tan-Khiem, Egan, Malcom, Neglia, Giovanni, Gorce, Jean-Marie 2026 置信度 0.66
• Streaming Federated Learning with Markovian Data
  datacite Huynh, Tan-Khiem, Egan, Malcom, Neglia, Giovanni, Gorce, Jean-Marie 2026 置信度 0.66
• APPLICATION OF ARTIFICIAL INTELLIGENCE IN REGULATORY COMPLIANCE FOR CROSS-BORDER DIGITAL TRANSACTIONS
  This study examines the theoretical, structural, and empirical applications of Artificial Intelligence (AI) and Machine Learning (ML) architectures within the domain of regulatory compliance (RegTech) and supervisory technology (SupTech) for cross-border digit…
  datacite Vakhabov Bobur 2026 置信度 0.66 Artificial Intelligence, Regulatory Compliance, Cross-Border Transactions, RegTech, Anti-Money Laundering (AML), Graph Neural Networks, Federated Learning, SupTech.
• APPLICATION OF ARTIFICIAL INTELLIGENCE IN REGULATORY COMPLIANCE FOR CROSS-BORDER DIGITAL TRANSACTIONS
  This study examines the theoretical, structural, and empirical applications of Artificial Intelligence (AI) and Machine Learning (ML) architectures within the domain of regulatory compliance (RegTech) and supervisory technology (SupTech) for cross-border digit…
  datacite Vakhabov Bobur 2026 置信度 0.66 Artificial Intelligence, Regulatory Compliance, Cross-Border Transactions, RegTech, Anti-Money Laundering (AML), Graph Neural Networks, Federated Learning, SupTech.
• AI-DRIVEN FEDERATED LEARNING FRAMEWORK FOR PRIVACY-PRESERVING EARLY DETECTION OF CYBER THREATS IN PAKISTAN'S CRITICAL INFRASTRUCTURE SYSTEMS
  datacite Shazia Paras Shaikh,Muhammad Suliman 2026 置信度 0.66
• AI-DRIVEN FEDERATED LEARNING FRAMEWORK FOR PRIVACY-PRESERVING EARLY DETECTION OF CYBER THREATS IN PAKISTAN'S CRITICAL INFRASTRUCTURE SYSTEMS
  datacite Shazia Paras Shaikh,Muhammad Suliman 2026 置信度 0.66
• Leveraging Natural Language Processing to Detect Non-Compliance in Clinical Documentation: Current Advances, Challenges, and Future Directions
  Clinical documentation is essential for patient safety, regulatory compliance, and healthcare quality, yet manual auditing of electronic health records (EHRs) remains labor-intensive and error prone. This paper reviews current advances in applying Natural Lang…
  datacite Salami, Aishat O. 2023 置信度 0.66 Natural Language ProcessingClinical DocumentationElectronic Health RecordsRegulatory ComplianceHealthcare Quality
• Leveraging Natural Language Processing to Detect Non-Compliance in Clinical Documentation: Current Advances, Challenges, and Future Directions
  Clinical documentation is essential for patient safety, regulatory compliance, and healthcare quality, yet manual auditing of electronic health records (EHRs) remains labor-intensive and error prone. This paper reviews current advances in applying Natural Lang…
  datacite Salami, Aishat O. 2023 置信度 0.66 Natural Language ProcessingClinical DocumentationElectronic Health RecordsRegulatory ComplianceHealthcare Quality
• FEDERATED MACHINE LEARNING AS A PATHWAY TO PRIVACY-PRESERVING ARTIFICIAL INTELLIGENCE IN SMART DIGITAL ECOSYSTEMS
  datacite Kainat Tariq,Syed Zeshan Haidar,Kohal deep,Muhammad Haqan Ali Rai 2026 置信度 0.66
• FEDERATED MACHINE LEARNING AS A PATHWAY TO PRIVACY-PRESERVING ARTIFICIAL INTELLIGENCE IN SMART DIGITAL ECOSYSTEMS
  datacite Kainat Tariq,Syed Zeshan Haidar,Kohal deep,Muhammad Haqan Ali Rai 2026 置信度 0.66
• Cross-Agent Governance Alignment Without Rule Disclosure: A Problem Formalization
  This preprint formalizes the Cross-Agent Governance Alignment (CAGA) problem: the challenge of verifying mutual governance compatibility between autonomous AI agents operating under distinct organizational policy regimes—without disclosing proprietary governan…
  datacite Meyman, Edward 2026 置信度 0.66 AI governanceMulti-agent systemsZero-knowledge proofsCross-organizational coordinationGovernance alignment
• PrivateBoost: privacy-preserving federated gradient boosting for single-record patient devices
  Deployable implementation of the PrivateBoost protocol: histogram-based federated gradient boosting in which every client holds a single labelled record, splits its gradient and Hessian statistics into Shamir shares over a Mersenne prime field, and distributes…
  datacite Specht, Bernhard, Ermis, Orhan, Garbaya, Samaher, Schneider, Reinhard 等 2026 置信度 0.66 federated learninggradient boostingsecret sharingprivacy-preserving machine learningcross-device learning
• PrivateBoost: privacy-preserving federated gradient boosting for single-record patient devices
  Deployable implementation of the PrivateBoost protocol: histogram-based federated gradient boosting in which every client holds a single labelled record, splits its gradient and Hessian statistics into Shamir shares over a Mersenne prime field, and distributes…
  datacite Specht, Bernhard, Ermis, Orhan, Garbaya, Samaher, Schneider, Reinhard 等 2026 置信度 0.66 federated learninggradient boostingsecret sharingprivacy-preserving machine learningcross-device learning
• Federated Learning for Melanoma Classification : Analysing Diverse Federated Approaches
  Federated learning has emerged as a revolutionary method for training machine learning models across disparate data sources. This method ensures that data privacy and security are maintained during the training process, which is especially important in sensiti…
  datacite Gandhi, Yatin Bharat, Mahobiya, Dr. Chandrakant 2024 置信度 0.66 Federated learningmelanomaFedAvgFedProxHealthcare.
• Federated Learning for Melanoma Classification : Analysing Diverse Federated Approaches
  Federated learning has emerged as a revolutionary method for training machine learning models across disparate data sources. This method ensures that data privacy and security are maintained during the training process, which is especially important in sensiti…
  datacite Gandhi, Yatin Bharat, Mahobiya, Dr. Chandrakant 2024 置信度 0.66 Federated learningmelanomaFedAvgFedProxHealthcare.
• Advanced Topics in AI & Data Science
  Abstract: Artificial Intelligence (AI) and Data Science have rapidly grown into revolutionary technologies that are both subjects of scientific research and are revolutionizing industries and decision-making process. Innovations like deep learning, generative …
  datacite M. G. Shrigan, S. D. Bhourgunde 2026 置信度 0.66
• Advanced Topics in AI & Data Science
  Abstract: Artificial Intelligence (AI) and Data Science have rapidly grown into revolutionary technologies that are both subjects of scientific research and are revolutionizing industries and decision-making process. Innovations like deep learning, generative …
  datacite M. G. Shrigan, S. D. Bhourgunde 2026 置信度 0.66
• One Shot Nested Pruning for Resource Constrained Federated Learning
  datacite Dejene, Nathnael 2026 置信度 0.66
• "A Multi-Environment Real-World Multi-Band RF Dataset for Spectrum Sensing and Occupancy Analysis for Allocations or Sharing"
  "Spectrum-sensing research is dominated by corpora that are either fully synthetic or captured under a single set of propagation and traffic conditions, then augmented with artificial impairments. This collection instead sweeps the same receiver, the same ante…
  datacite Atik Mahabub, Shervin Vakili 2026 置信度 0.66
• One Shot Nested Pruning for Resource Constrained Federated Learning
  datacite Dejene, Nathnael 2026 置信度 0.66
• Distributed Differential Privacy with Federated Learning via Lagrangian Relaxation
  Achieving strong differential privacy guarantees within the constraints of federated learning, especially when utilizing complex models, remains a significant challenge. This work proposes a novel framework leveraging Lagrangian relaxation to address this issu…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Distributed Differential Privacy with Federated Learning via Lagrangian Relaxation
  Achieving strong differential privacy guarantees within the constraints of federated learning, especially when utilizing complex models, remains a significant challenge. This work proposes a novel framework leveraging Lagrangian relaxation to address this issu…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Artificial Intelligence in Pharmaceutical Innovation and Research: A Review
  Artificial intelligence (AI) is revolutionizing pharmaceutical research by accelerating and refining drug discovery, development, and clinical practice. This review summarizes the role of AI across key stages of the pharmaceutical pipeline, including target id…
  datacite Rahul kr. Rai*1, Sharad Suman2, Priya Raj3, Siddharth Kowsik4 2026 置信度 0.66 Artificial Intelligence, Machine Learning, Drug Discovery, Pharmaceutical Research, Predictive Modeling, Big Data
• Artificial Intelligence in Pharmaceutical Innovation and Research: A Review
  Artificial intelligence (AI) is revolutionizing pharmaceutical research by accelerating and refining drug discovery, development, and clinical practice. This review summarizes the role of AI across key stages of the pharmaceutical pipeline, including target id…
  datacite Rahul kr. Rai*1, Sharad Suman2, Priya Raj3, Siddharth Kowsik4 2026 置信度 0.66 Artificial Intelligence, Machine Learning, Drug Discovery, Pharmaceutical Research, Predictive Modeling, Big Data
• claudy667/esp32-federated-lstm-communitynet-: CommunityNet PF-LSTM — Initial Release
  Initial release of the PF-LSTM federated learning codebase for offline ESP32 community mesh networks. KNUST CS Research 2026.
  datacite claudy667 2026 置信度 0.66
• claudy667/esp32-federated-lstm-communitynet-: CommunityNet PF-LSTM — Initial Release
  Initial release of the PF-LSTM federated learning codebase for offline ESP32 community mesh networks. KNUST CS Research 2026.
  datacite claudy667 2026 置信度 0.66
• rdelhibabu/Automated-Clinical-Documentation_Intelligent-Rooms: V1
  Generative AI at the Edge: A Privacy-Preserving Federated Learning Architecture for Automated Clinical Documentation in Intelligent Rooms
  datacite Radhakrishnan Delhibabu 2026 置信度 0.66
• rdelhibabu/Automated-Clinical-Documentation_Intelligent-Rooms: V1
  Generative AI at the Edge: A Privacy-Preserving Federated Learning Architecture for Automated Clinical Documentation in Intelligent Rooms
  datacite Radhakrishnan Delhibabu 2026 置信度 0.66
• Validator-Backed Auditable Robust Aggregation for Trustworthy Federated Learning
  Neurocomputing reproducibility package for: Validator-Backed Auditable Robust Aggregation for Trustworthy Federated Learning This release archives the code, configurations, result summaries, paper figures, supplementary tables, validator-audit outputs, and aud…
  datacite Huang, Shiqi 2026 置信度 0.66
• Validator-Backed Auditable Robust Aggregation for Trustworthy Federated Learning
  Neurocomputing reproducibility package for: Validator-Backed Auditable Robust Aggregation for Trustworthy Federated Learning This release archives the code, configurations, result summaries, paper figures, supplementary tables, validator-audit outputs, and aud…
  datacite Huang, Shiqi 2026 置信度 0.66
• Formal Verification of Trustworthy Federated Learning Systems
  Federated learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data, offering enhanced privacy and reduced communication costs. However, the inherent distributed nature of FL introduces significant challenges …
  datacite Zhang, Jincheng 2026 置信度 0.66
• Formal Verification of Trustworthy Federated Learning Systems
  Federated learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data, offering enhanced privacy and reduced communication costs. However, the inherent distributed nature of FL introduces significant challenges …
  datacite Zhang, Jincheng 2026 置信度 0.66
• Cross-Agent Governance Alignment (CAGA): Formalizing Cross-Organizational AI Governance as a Zero-Knowledge Coordination Problem
  This preprint formalizes the Cross-Agent Governance Alignment (CAGA) problem: the challenge of verifying mutual governance compatibility between autonomous AI agents operating under distinct organizational policy regimes, without disclosing proprietary governa…
  datacite Meyman, Edward 2026 置信度 0.66 AI governanceMulti-agent systemsZero-knowledge proofsCross-organizational coordinationGovernance alignment
• Trusted Federated Learning XAI: Open Source for Privacy-Preserving Explanations
  Research article: Trusted Federated Learning XAI: Open Source for Privacy-Preserving Explanations
  datacite Ivchenko, Oleh, Ivchenko, Iryna 2026 置信度 0.66 AImachine learningresearch
• Trusted Federated Learning XAI: Open Source for Privacy-Preserving Explanations
  Research article: Trusted Federated Learning XAI: Open Source for Privacy-Preserving Explanations
  datacite Ivchenko, Oleh, Ivchenko, Iryna 2026 置信度 0.66 AImachine learningresearch
• Decentralized Federated Learning with Secure Aggregation and Differential Privacy
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL schemes are vulnerable to privacy attacks, particularly model inversion attacks, which ca…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Decentralized Federated Learning with Secure Aggregation and Differential Privacy
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL schemes are vulnerable to privacy attacks, particularly model inversion attacks, which ca…
  datacite Zhang, Jincheng 2026 置信度 0.66
• GROUPOID: Groupoid-based aggregation for federated learning on Riemannian manifolds
  Pre-alpha research prototype exploring groupoid-based aggregation for federated learning on Riemannian manifolds. Implements transport groupoid morphisms, first cohomology (H^1) for consistency detection, the cellular sheaf Laplacian for spectral analysis, par…
  datacite Maniches, Santiago 2026 置信度 0.66 federated learningRiemannian geometrygroupoidsheaf theorycohomology
• GROUPOID: Groupoid-based aggregation for federated learning on Riemannian manifolds
  Pre-alpha research prototype exploring groupoid-based aggregation for federated learning on Riemannian manifolds. Implements transport groupoid morphisms, first cohomology (H^1) for consistency detection, the cellular sheaf Laplacian for spectral analysis, par…
  datacite Maniches, Santiago 2026 置信度 0.66 federated learningRiemannian geometrygroupoidsheaf theorycohomology
• Federated Learning with Differential Privacy for Personalized Model Training
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging raw data. However, traditional FL methods still face significant privacy risks, particularly when dealing with sen…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Federated Learning with Differential Privacy for Personalized Model Training
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging raw data. However, traditional FL methods still face significant privacy risks, particularly when dealing with sen…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Differential Privacy for Federated Learning
  This paper explores the application of differential privacy to federated learning, a distributed machine learning paradigm gaining prominence for its potential to leverage decentralized data while preserving privacy. Federated learning enables training of mach…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Differential Privacy for Federated Learning
  This paper explores the application of differential privacy to federated learning, a distributed machine learning paradigm gaining prominence for its potential to leverage decentralized data while preserving privacy. Federated learning enables training of mach…
  datacite Zhang, Jincheng 2026 置信度 0.66
• CollaFuse: Collaborative Diffusion Models
  In the landscape of generative artificial intelligence, diffusion-based models have emerged as a promising method for generating synthetic images. However, the application of diffusion models poses numerous challenges, particularly concerning data availability…
  datacite Allmendinger, Simeon, Zipperling, Domenique, Struppek, Lukas, Kühl, Niklas 2024 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciences
• Federated Adversarial Training with Transformers
  Federated learning (FL) has emerged to enable global model training over distributed clients' data while preserving its privacy. However, the global trained model is vulnerable to the evasion attacks especially, the adversarial examples (AEs), carefully crafte…
  datacite Aldahdooh, Ahmed, Hamidouche, Wassim, Déforges, Olivier 2022 置信度 0.66 Machine Learning (cs.LG)Cryptography and Security (cs.CR)Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciences
• Decentralized Federated Learning with Differential Privacy for Edge Computing
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL methods often rely on a central server, raising significant privacy concerns, especi…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Decentralized Federated Learning with Differential Privacy for Edge Computing
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL methods often rely on a central server, raising significant privacy concerns, especi…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Personalized Federated Learning for Tensor Regression
  The growing availability of tensor-valued data across multiple institutions creates opportunities for collaborative analysis, but also raises challenges related to data privacy, high dimensionality, and client heterogeneity. This paper introduces a personalize…
  datacite Chen, Kejun, Wei, Xianqi, Zhu, Qianqian 2026 置信度 0.66 Methodology (stat.ME)FOS: Computer and information sciences
• SecureDrive-FL: Joint Differential Privacy and Gradient-Aware Selective Homomorphic Encryption for Federated Driver Monitoring
  Federated Learning (FL) enables privacy-aware distributed training, yet gradient updates remain exploitable: Man-in-the-Middle (MitM) interception exposes updates in transit, while model poisoning corrupts global convergence. We first introduce GASHE (Gradient…
  datacite Gül, Baran Can, Tunuguntla, Hanuma Siddhartha, Naik, Anjana Arvind, Potekar, Abhishek Vijay 等 2026 置信度 0.66 Cryptography and Security (cs.CR)Machine Learning (cs.LG)FOS: Computer and information sciences
• An Accurate and Single-Communication Federated Inference Algorithm
  Joint analyses across multiple institutions are increasingly important in biomedical and epidemiological research, particularly for rare diseases where datasets are typical small. However, privacy regulations and institutional policies often prevent the sharin…
  datacite Montagnani, Laura, Coolen, Anthony CC, Jonker, Marianne A 2026 置信度 0.66 Methodology (stat.ME)Statistics Theory (math.ST)Computation (stat.CO)Machine Learning (stat.ML)FOS: Computer and information sciences
• Decentralized Federated Learning with Byzantine Fault Tolerance
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly sharing the data itself. However, this decentralized nature introduces significant vulnerabilities. Malicious participants, k…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Decentralized Federated Learning with Byzantine Fault Tolerance
  Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly sharing the data itself. However, this decentralized nature introduces significant vulnerabilities. Malicious participants, k…
  datacite Zhang, Jincheng 2026 置信度 0.66
• The Future of Artificial Intelligence in Breast Cancer Diagnosis and Prognosis: Trends, Innovations, and Breakthroughs
  Breast cancer remains one of the most significant health challenges worldwide, requiring continuous advancements in early detection, diagnosis, and prognosis. With the rapid evolution of artificial intelligence (AI), the landscape of breast cancer management h…
  datacite Dogra, Ashok Kumar, Prakash, Archana, Gupta, Meenu 2025 置信度 0.66 breast cancerAI in breast cancer
• The Future of Artificial Intelligence in Breast Cancer Diagnosis and Prognosis: Trends, Innovations, and Breakthroughs
  Breast cancer remains one of the most significant health challenges worldwide, requiring continuous advancements in early detection, diagnosis, and prognosis. With the rapid evolution of artificial intelligence (AI), the landscape of breast cancer management h…
  datacite Dogra, Ashok Kumar, Prakash, Archana, Gupta, Meenu 2025 置信度 0.66 breast cancerAI in breast cancer
• AGIS-NG: AI-Powered Counter-UAS and Autonomous Border Surveillance for Nigeria's Insecure Border Regions Radar-Vision Fusion, RF Signal Classification, and Autonomous Patrol Optimisation for the Lake Chad Basin and Northwest Border Corridors
  Nigeria faces an intensifying dual threat: Boko Haram/ISWAP insurgents operating armed drones in the Lake Chad Basin and Northwest armed bandits exploiting surveillance-free border corridors spanning more than 1,400 km of Nigeria’s insecure border regions. The…
  datacite Sulaiman, J., Hussain, M. M. A. 2026 置信度 0.66
• AGIS-NG: AI-Powered Counter-UAS and Autonomous Border Surveillance for Nigeria's Insecure Border Regions Radar-Vision Fusion, RF Signal Classification, and Autonomous Patrol Optimisation for the Lake Chad Basin and Northwest Border Corridors
  Nigeria faces an intensifying dual threat: Boko Haram/ISWAP insurgents operating armed drones in the Lake Chad Basin and Northwest armed bandits exploiting surveillance-free border corridors spanning more than 1,400 km of Nigeria’s insecure border regions. The…
  datacite Sulaiman, J., Hussain, M. M. A. 2026 置信度 0.66
• FedCMAPSS: A Benchmark for Federated Learning in Remaining Useful Life Estimation
  Data-driven prognostics and health management has emerged as a key enabler for Industry 4.0, yet the development of robust remaining useful life (RUL) estimation models is often limited by the scarcity of run-to-failure data. While federated learning offers a …
  datacite Sorrenti, Amelia, Pennisi, Matteo, Spampinato, Concetto, Palazzo, Simone 2026 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciences
• Byzantine-Resilient Aggregation Algorithms in Federated Malware Detection for IoT Networks
  This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How do different aggregation algorithms (FedAvg, FedProx, FedNova) compare in terms of model accuracy degradation when defending against Byzantine attacks…
  datacite Assignee Research 2026 置信度 0.66 differentaggregationalgorithmsFedAvgFedProx
• Byzantine-Resilient Aggregation Algorithms in Federated Malware Detection for IoT Networks
  This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How do different aggregation algorithms (FedAvg, FedProx, FedNova) compare in terms of model accuracy degradation when defending against Byzantine attacks…
  datacite Assignee Research 2026 置信度 0.66 differentaggregationalgorithmsFedAvgFedProx
• AeroGuard-EP: An Integrated Stochastic Multi-Agent Protocol for Cross-Platform Information Resilience and Algorithmic Circuit Breaking
  The rapid evolution of the digital information ecosystem has transformed Social Media Platforms (SMPs) into critical yet vulnerable nodes of the global infrastructure. Current mitigation strategies are largely reactive, struggling to contain the "virality logi…
  datacite Mr.Diwakara Vasuman, Karthik S Gowda, Rohith R, Vipul Mahesh 2026 置信度 0.66
• AeroGuard-EP: An Integrated Stochastic Multi-Agent Protocol for Cross-Platform Information Resilience and Algorithmic Circuit Breaking
  The rapid evolution of the digital information ecosystem has transformed Social Media Platforms (SMPs) into critical yet vulnerable nodes of the global infrastructure. Current mitigation strategies are largely reactive, struggling to contain the "virality logi…
  datacite Mr.Diwakara Vasuman, Karthik S Gowda, Rohith R, Vipul Mahesh 2026 置信度 0.66
• Generative Threats in Computer Vision: Dual-Role Gans and Diffusion-Based Defenses for Robust and Trustworthy Models
  Abstract - Generative Artificial Intelligence (GenAI) has revolutionized computer vision with cutting-edge image generation, semantic interpretation, and adaptive visual reasoning capabilities, while also bringing new security, robustness, and trustworthiness …
  datacite Mahesh Kumar, Tanvi Rustagi 2026 置信度 0.66 Generative Artificial IntelligenceGenerative Adversarial NetworksDiffusion ModelsComputer Vision SecurityAdversarial Attacks
• Generative Threats in Computer Vision: Dual-Role Gans and Diffusion-Based Defenses for Robust and Trustworthy Models
  Abstract - Generative Artificial Intelligence (GenAI) has revolutionized computer vision with cutting-edge image generation, semantic interpretation, and adaptive visual reasoning capabilities, while also bringing new security, robustness, and trustworthiness …
  datacite Mahesh Kumar, Tanvi Rustagi 2026 置信度 0.66 Generative Artificial IntelligenceGenerative Adversarial NetworksDiffusion ModelsComputer Vision SecurityAdversarial Attacks
• Privacy-Preserving Machine Learning for Healthcare: A Comparative Study of Federated Learning, Split Learning, and Split-Fed Learning
  This project presents an empirical comparison of three privacy-preserving machine learning (PPML) techniques — Federated Learning (FL), Split Learning (SL), and Split-Fed Learning (SFL) — for healthcare classification, benchmarked against a centralised baselin…
  datacite Sathar, Alif 2026 置信度 0.66 Privacy-Preserving Machine LearningFederated LearningSplit LearningSplit-Fed LearningHealthcare AI
• Privacy-Preserving Machine Learning for Healthcare: A Comparative Study of Federated Learning, Split Learning, and Split-Fed Learning
  This project presents an empirical comparison of three privacy-preserving machine learning (PPML) techniques — Federated Learning (FL), Split Learning (SL), and Split-Fed Learning (SFL) — for healthcare classification, benchmarked against a centralised baselin…
  datacite Sathar, Alif 2026 置信度 0.66 Privacy-Preserving Machine LearningFederated LearningSplit LearningSplit-Fed LearningHealthcare AI
• HYDROCAST-NG: Real-Tıme Flood Mappıng and Dısplaced Populatıon Estımatıon Usıng Sentınel-1 SAR Imagery and Deep Learnıng for Nıgerıa: A Semantic Segmentation and Spatio-Temporal Forecasting Framework for Disaster Response and Humanitarian Planning
  Nigeria experiences some of the most severe recurrent flooding in sub-Saharan Africa. The 2022 floods alone displaced 1.4 million people, destroyed 82,053 homes, and caused an estimated USD 4.2 billion in economic damage across 34 of 36 states. Existing early …
  datacite Sulaiman, J., Hussain, M. M. A. 2026 置信度 0.66
• HYDROCAST-NG: Real-Tıme Flood Mappıng and Dısplaced Populatıon Estımatıon Usıng Sentınel-1 SAR Imagery and Deep Learnıng for Nıgerıa: A Semantic Segmentation and Spatio-Temporal Forecasting Framework for Disaster Response and Humanitarian Planning
  Nigeria experiences some of the most severe recurrent flooding in sub-Saharan Africa. The 2022 floods alone displaced 1.4 million people, destroyed 82,053 homes, and caused an estimated USD 4.2 billion in economic damage across 34 of 36 states. Existing early …
  datacite Sulaiman, J., Hussain, M. M. A. 2026 置信度 0.66
• Federated vs Centralized Malware Detection Robustness Under Adversarial IoT Attacks
  This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does the robustness of federated malware detection models compare to centralized models when tested against adversarial attacks on edge IoT devices un…
  datacite Assignee Research 2026 置信度 0.66 robustnessfederatedmalwaredetectionmodels
• Federated vs Centralized Malware Detection Robustness Under Adversarial IoT Attacks
  This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does the robustness of federated malware detection models compare to centralized models when tested against adversarial attacks on edge IoT devices un…
  datacite Assignee Research 2026 置信度 0.66 robustnessfederatedmalwaredetectionmodels
• Scalable Real-Time Android Malware Detection Using Adaptive Deep Learning and Ensemble Techniques
  Android malware is constantly evolving, and operational constraints, and implementing federated learning protocols to ensure that model updates remain localized to preserve user data integrity while constantly improving detection capabilities against emerging …
  datacite S. Gnanadeep Srinivas, Dr. A. Ganesh 2026 置信度 0.66 Android Malware DetectionConvolutional Neural Networks (CNN)Frequency Domain AnalysisFeature FusionAdaptive Feature Selection
• Scalable Real-Time Android Malware Detection Using Adaptive Deep Learning and Ensemble Techniques
  Android malware is constantly evolving, and operational constraints, and implementing federated learning protocols to ensure that model updates remain localized to preserve user data integrity while constantly improving detection capabilities against emerging …
  datacite S. Gnanadeep Srinivas, Dr. A. Ganesh 2026 置信度 0.66 Android Malware DetectionConvolutional Neural Networks (CNN)Frequency Domain AnalysisFeature FusionAdaptive Feature Selection
• Adaptive Sparsification and FedAvg for Low-Latency LLM Inference in Federated Edge Networks
  This report synthesises findings from 10 peer-reviewed papers addressing the following research question: Can adaptive sparsification techniques combined with FedAvg improve inference latency and throughput for large language models in over-the-air federated l…
  datacite Assignee Research 2026 置信度 0.66 adaptivesparsificationtechniquescombinedFedAvg
• Adaptive Sparsification and FedAvg for Low-Latency LLM Inference in Federated Edge Networks
  This report synthesises findings from 10 peer-reviewed papers addressing the following research question: Can adaptive sparsification techniques combined with FedAvg improve inference latency and throughput for large language models in over-the-air federated l…
  datacite Assignee Research 2026 置信度 0.66 adaptivesparsificationtechniquescombinedFedAvg
• Secure AI Agent-Driven Conversational Support in Healthcare Integrating Cybersecurity from Diagnostics to Patient Coaching
  Industrial American styles of capitalism have reified AI as an industry of speculative economy, thereby accelerating the agency with which these agents are adopted into systems of care and further deferred responsibility for patient engagement, clinical diagno…
  datacite pulipati, karthik, Goshikonda, Nagaraju 2023 置信度 0.66
• Secure AI Agent-Driven Conversational Support in Healthcare Integrating Cybersecurity from Diagnostics to Patient Coaching
  Industrial American styles of capitalism have reified AI as an industry of speculative economy, thereby accelerating the agency with which these agents are adopted into systems of care and further deferred responsibility for patient engagement, clinical diagno…
  datacite pulipati, karthik, Goshikonda, Nagaraju 2023 置信度 0.66
• AI-Driven Payment Personalization and Smart Payment Assistants: Reshaping the Digital Payment Landscape
  AI-driven payment personalization and smart payment assistants represent a transformative advancement in financial technology, merging sophisticated machine learning models with traditional banking infrastructure. These intelligent systems optimize transaction…
  datacite Das, Priya 2025 置信度 0.66 Authentication; Encryption; Microservices; Personalization; Transaction
• AI-Driven Payment Personalization and Smart Payment Assistants: Reshaping the Digital Payment Landscape
  AI-driven payment personalization and smart payment assistants represent a transformative advancement in financial technology, merging sophisticated machine learning models with traditional banking infrastructure. These intelligent systems optimize transaction…
  datacite Das, Priya 2025 置信度 0.66 Authentication; Encryption; Microservices; Personalization; Transaction
• Efficient Federated Learning Techniques for Data Loss Prevention in Cloud Environment
  crossref Peter Soosai A. Anandaraj, S. Sridevi, R. Vaishnavi, M. Meenalakshimi 等 2023-11-16T11:04:28Z 置信度 0.70
• Privacy‐Aware Machine Learning for Sustainable Farming
  crossref Mithaguru, Sugandha Saxena, Mude Nagarjuna Naik, R. Sriramkumar 等 2026-06-23T12:51:06Z 置信度 0.70
• Fundamentals of Federated Learning
  crossref Choong Seon Hong, Latif U. Khan, Mingzhe Chen, Dawei Chen 等 2022-01-01T05:02:09Z 置信度 0.70
• Mitigating One-to-N Backdoor Attacks in Federated Learning
  crossref Osama Wehbi, Sarhad Arisdakessian, Omar Abdel Wahab, Azzam Mourad 等 2026-07-29T19:14:27Z 置信度 0.70
• Adaptive training and aggregation for federated learning in multi-tier computing networks
  crossref Wenjing Hou, Hong Wen, Ning Zhang, Wenxin Lei 等 2026-06-12T10:49:31Z 置信度 0.70
• Towards Self-Evolving Non-Terrestrial Networks (NTN) via Federated Learning
  crossref Amin Farajzadeh 2026-01-19T15:59:01Z 置信度 0.70
• Figure 8: Randomization based federated deep learning for securing energy data computation.
  crossref 2026-05-01T08:00:31Z 置信度 0.70
• Application of Federated Learning in Medical Imaging
  crossref Ehsan Degan, Shafiq Abedin, David Beymer, Angshuman Deb 等 2022-07-07T12:16:52Z 置信度 0.70
• Traffic Flow Distribution Forecast with Federated Learning and Bayesian Deep Learning
  Abstract Traffic flow distribution forecasting plays a crucial role in intelligent transportation systems for effective traffic management and congestion mitigation. In this paper, we propose a novel approach for traffic flow distribution forecasting using fed…
  crossref Zhou Eric Shen 2023-07-18T05:57:55Z 置信度 0.70
• Privacy-Preserving Personalized Seizure Risk Monitoring Using Reinforcement Learning and Federated Learning on Wearable Data
  crossref Mayank Kapadia 2026-07-31T15:10:19Z 置信度 0.70
• Federated Learning in Food Inspection and Grading
  crossref Reeta Mishra, Padmesh Tripathi, Reddy Saisindhutheja, Gagandeep Arora 等 2025-12-15T10:17:52Z 置信度 0.70
• Heterogeneity‐Aware Dynamic Scheduling for Federated Edge Learning
  crossref Kun Guo, Zihan Chen, Howard H. Yang, Tony Q. S. Quek 2023-12-02T00:27:27Z 置信度 0.70
• Growing Domain-Specific LLMs Through Federated Split-Phase Learning: System Design and Analysis
  crossref Silin Zhao, Sadegh Keshtkar 2026-07-29T19:14:10Z 置信度 0.70
• Federated Learning Showdown: The Comparative Analysis of Federated Learning Frameworks
  crossref Sai Praneeth Karimireddy, Narasimha Raghavan Veeraragavan, Severin Elvatun, Jan F. Nygård 2023-11-08T13:51:37Z 置信度 0.70
• Introduction to Federated Learning and Threat Landscape
  Federated Learning (FL), a collaborative machine learning paradigm that allows model training across multiple devices without centralized data aggregation, was developed in response to the exponential growth of decentralized data across mobile devices, edge se…
  crossref Rakesh Kumar Saxena, Shikha Khullar 2026-02-20T15:26:17Z 置信度 0.70
• Optimized Migraine Detection in Healthcare: Exploring Random Forest and XGBoost with Prospects for Federated Learning
  crossref Tanisha Dhoot, K.S. Archana, R. Anandan, Tahsheen Fatima 2026-05-08T21:41:08Z 置信度 0.70
• Federated Learning for ICU Mortality Prediction: Balancing Accuracy and Privacy in a Multi-Hospital Setting
  crossref Yassir Benhammou, Suman Kalyan, Sujay Kumar 2026-07-29T19:11:33Z 置信度 0.70
• FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning
  crossref Lingyu Qiu, Daniela Annunziata, Stefano Izzo, Fabio Giampaolo 等 2026-07-29T19:13:54Z 置信度 0.70
• Figure 9: Fairness and performance comparison using reputation management in federated learning.
  crossref 2026-02-04T08:57:09Z 置信度 0.70
• Federated multi-cloud task scheduling with load balancing using multi-objective NSGA-II and reinforcement learning.
  Task scheduling in federated multi-cloud environments is challenging owing to heterogeneous service-level agreements, decentralized resource control, and dynamic workload characteristics. The existing hybrid optimization approaches lack real-time adaptability …
  pubmed Ghaban W, Alatawi HS 2026 置信度 0.82
• FL-trafficNet: a hybrid federated transformer-GNN framework with dual attention for smart routing in IoV and VANET environments.
  One of the major problems smart cities face is how to efficiently route traffic, especially when connected vehicles and sensors produce a very large amount of real, time data. This heavy traffic load results in delays, inefficient routing, and excessive proces…
  pubmed Narsimhulu P, Sahay R 2026 May 24 置信度 0.82