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• AMLNet: A Decentralised Anti-Money Laundering Detection Framework Using Federated Learning, Blockchain, and Zero-Knowledge Proofs
  One of these financial crimes, which seem to sound like a concept straight out of a dream until you get a sense of the magnitude of the issue, is money laundering. According to the United Nations, Between $800 billion and $2 trillion in illicit money is transa…
  datacite Priya S, Dakshayini M, Apsana S A, Anjana M R 2026 置信度 0.66
• Artificial Intelligence: From Fundamentals to Real-World Applications BY Ts. Dr. Sundresan Perumal, Dr. K. Appathurai, Dr. B. Sathya Bama, Dr. P. Ajitha.
  Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the twenty-first century, fundamentally reshaping the way individuals, organizations, industries, and societies operate. From intelligent virtual assistants and autonomo…
  datacite Ts. Dr. Sundresan Perumal, Dr. K. Appathurai, Dr. B. Sathya Bama, Dr. P. Ajitha. 2026 置信度 0.66
• Artificial Intelligence: From Fundamentals to Real-World Applications BY Ts. Dr. Sundresan Perumal, Dr. K. Appathurai, Dr. B. Sathya Bama, Dr. P. Ajitha.
  Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the twenty-first century, fundamentally reshaping the way individuals, organizations, industries, and societies operate. From intelligent virtual assistants and autonomo…
  datacite Ts. Dr. Sundresan Perumal, Dr. K. Appathurai, Dr. B. Sathya Bama, Dr. P. Ajitha. 2026 置信度 0.66
• Computer-Assisted Resolution of the Direct Product Conjecture via Kullback-Leibler (KL) Divergence Information Tensorization and Pinsker-Bounded Simulation Operators (Cloud Security)
  Using the Lean 4 interactive theorem prover, this paper presents a machine-verified structural resolution of the Direct Product Conjecture in communication complexity. Extending Hilbert space orthogonal projections and ANOVA-Hoeffding decompositions beyond lin…
  datacite Reed, Jonathan ƒ(n) 2026 置信度 0.66 Computer and information sciencesTheoretical Computer ScienceComplexity TheoryStatistics and probabilityProbability Theory
• Computer-Assisted Resolution of the Direct Product Conjecture via Kullback-Leibler (KL) Divergence Information Tensorization and Pinsker-Bounded Simulation Operators (Cloud Security)
  Using the Lean 4 interactive theorem prover, this paper presents a machine-verified structural resolution of the Direct Product Conjecture in communication complexity. Extending Hilbert space orthogonal projections and ANOVA-Hoeffding decompositions beyond lin…
  datacite Reed, Jonathan ƒ(n) 2026 置信度 0.66 Computer and information sciencesTheoretical Computer ScienceComplexity TheoryStatistics and probabilityProbability Theory
• TOWARDS PROACTIVE CYBER DEFENSE CYBER ATTACK PREDICTION USING ADVANCED AI TECHNIQUES
  Develop an AI-based predictive analytics platform that is capable of identifying potential cyber threats. In real time, transformer topologies, deep learning, and graph neural networks replicate complex, high-dimensional security data streams. Temporal sequenc…
  datacite International Journal of Technovation and Business Insights 2026 置信度 0.66 Predictive AnalyticsCyber Attack DetectionNext-Generation AIDeep LearningGraph Neural Networks
• TOWARDS PROACTIVE CYBER DEFENSE CYBER ATTACK PREDICTION USING ADVANCED AI TECHNIQUES
  Develop an AI-based predictive analytics platform that is capable of identifying potential cyber threats. In real time, transformer topologies, deep learning, and graph neural networks replicate complex, high-dimensional security data streams. Temporal sequenc…
  datacite International Journal of Technovation and Business Insights 2026 置信度 0.66 Predictive AnalyticsCyber Attack DetectionNext-Generation AIDeep LearningGraph Neural Networks
• A Drift-Stable Quantum Federated Learning for Intelligent Services
  Quantum federated learning enables distributed clients to train quantum neural networks without sharing local data, making it promising for privacy-aware intelligent services. Intelligent services in this context refer to privacy-sensitive distributed decision…
  datacite Nanayakkara, shanika, Pokhrel, Shiva 2026 置信度 0.66
• A Drift-Stable Quantum Federated Learning for Intelligent Services
  Quantum federated learning enables distributed clients to train quantum neural networks without sharing local data, making it promising for privacy-aware intelligent services. Intelligent services in this context refer to privacy-sensitive distributed decision…
  datacite Nanayakkara, shanika, Pokhrel, Shiva 2026 置信度 0.66
• Decentralized Security Mechanisms for AI-Driven Wireless Networks : Integrating Blockchain and Federated Learning
  This dissertation explores how blockchain and federated learning can be wedded to create decentralized security measures for AI‑driven wireless networks, with a keen eye on pressing issues like data privacy and integrity. It dives into plenty of quantitative c…
  datacite Kataria, Bhavesh, Jethva, Harikrishna B. 2025 置信度 0.66 Blockchain; Federated Learning; Decentralized Security; Data Privacy; Healthcare Cybersecurity
• Decentralized Security Mechanisms for AI-Driven Wireless Networks : Integrating Blockchain and Federated Learning
  This dissertation explores how blockchain and federated learning can be wedded to create decentralized security measures for AI‑driven wireless networks, with a keen eye on pressing issues like data privacy and integrity. It dives into plenty of quantitative c…
  datacite Kataria, Bhavesh, Jethva, Harikrishna B. 2025 置信度 0.66 Blockchain; Federated Learning; Decentralized Security; Data Privacy; Healthcare Cybersecurity
• Learning Where the Data Lives: A Narrative Review of Federated Learning from Differential Privacy to the Open Problems
  Federated learning---training a shared model across many devices that never surrender their data---inverted machine learning's architecture: instead of data to the model, the model to the data. This article presents a narrative review of that arc's canonical l…
  datacite Revista, Zen, IA, 10 2026 置信度 0.66 federated learningFedAvgdifferential privacysecure aggregationnon-IID data
• Learning Where the Data Lives: A Narrative Review of Federated Learning from Differential Privacy to the Open Problems
  Federated learning---training a shared model across many devices that never surrender their data---inverted machine learning's architecture: instead of data to the model, the model to the data. This article presents a narrative review of that arc's canonical l…
  datacite Revista, Zen, IA, 10 2026 置信度 0.66 federated learningFedAvgdifferential privacysecure aggregationnon-IID data
• Privacy-Preserving Local LLM Inference for Developer Tooling — AI, LLM, Privacy, Sovereign AI, and Post-Cloud Architecture (Anticode)
  The proliferation of large language models (LLMs) in developer tooling has introduced significant privacy and security concerns, particularly when code---often containing proprietary algorithms, credentials, and business logic---is transmitted to remote infere…
  datacite Alpasan, Lois-Kleinner 2026 置信度 0.66 sovereign aisovereign datasovereign osoperating systemspost-cloud
• Privacy-Preserving Local LLM Inference for Developer Tooling — AI, LLM, Privacy, Sovereign AI, and Post-Cloud Architecture (Anticode)
  The proliferation of large language models (LLMs) in developer tooling has introduced significant privacy and security concerns, particularly when code---often containing proprietary algorithms, credentials, and business logic---is transmitted to remote infere…
  datacite Alpasan, Lois-Kleinner 2026 置信度 0.66 sovereign aisovereign datasovereign osoperating systemspost-cloud
• Artificial Intelligence in Biomedical Engineering: Current Advances and Future Perspectives in the United States
  Abstract Over the past few years, Artificial Intelligence (AI) has been a game-changer in biomedical engineering, transforming the design, validation and deployment of diagnostic, therapeutic, and monitoring systems across the U.S. healthcare landscape. In thi…
  datacite Evan Samuel, Walker 2025 置信度 0.66
• Artificial Intelligence in Biomedical Engineering: Current Advances and Future Perspectives in the United States
  Abstract Over the past few years, Artificial Intelligence (AI) has been a game-changer in biomedical engineering, transforming the design, validation and deployment of diagnostic, therapeutic, and monitoring systems across the U.S. healthcare landscape. In thi…
  datacite Evan Samuel, Walker 2025 置信度 0.66
• AuraOS Paper X Rev.3: A Regenerative, Model-Orthogonal, Source-Bound Cognitive Operating Substrate - Relational World Compilation, Coordinate Memory, HyperScale/ HyperDrive, Runtime Arenas, Proof-Carrying Commons, Recursive Swarms, Universal Host Compilation, and Semantic-Spatial Interfaces
  Executive Overview This consolidated release of Paper X unifies empirical findings, mathematical foundations, and real-world implementation proofs for AuraOS—a local-first, zero-extraction computational architecture designed to eliminate recurring cloud SaaS o…
  datacite Courchene, Dallas 2026 置信度 0.66
• AuraOS Paper X Rev.3: A Regenerative, Model-Orthogonal, Source-Bound Cognitive Operating Substrate - Relational World Compilation, Coordinate Memory, HyperScale/ HyperDrive, Runtime Arenas, Proof-Carrying Commons, Recursive Swarms, Universal Host Compilation, and Semantic-Spatial Interfaces
  Executive Overview This consolidated release of Paper X unifies empirical findings, mathematical foundations, and real-world implementation proofs for AuraOS—a local-first, zero-extraction computational architecture designed to eliminate recurring cloud SaaS o…
  datacite Courchene, Dallas 2026 置信度 0.66
• QAFL-ZT: Quantum-Enhanced Adversarial-Aware Federated Learning for Zero-Trust Network Intrusion Detection
  In cyberattacks and distributed computing environments era network intrusion detection systems has faced unprecedented challenges. There are some struggles with traditional centralized approaches that has some concern privacy concerns, computational scalabilit…
  datacite Zwayyer, Mustafa Hussein 2025 置信度 0.66 Federated Learning; Quantum Machine Learning; Adversarial Training; Zero-Trust Architecture; Network Intrusion Detection; Non-IID Data; Cybersecurity
• QAFL-ZT: Quantum-Enhanced Adversarial-Aware Federated Learning for Zero-Trust Network Intrusion Detection
  In cyberattacks and distributed computing environments era network intrusion detection systems has faced unprecedented challenges. There are some struggles with traditional centralized approaches that has some concern privacy concerns, computational scalabilit…
  datacite Zwayyer, Mustafa Hussein 2025 置信度 0.66 Federated Learning; Quantum Machine Learning; Adversarial Training; Zero-Trust Architecture; Network Intrusion Detection; Non-IID Data; Cybersecurity
• A Review : Enhancing Cybersecurity with Quantum Cryptography and Federated SMOTE-ADA Boost Framework for Detecting Credit Card Fraud
  The rapid growth of digital financial transactions has significantly increased the risk of credit card fraud, making robust cybersecurity mechanisms essential for modern banking systems. Traditional fraud detection approaches often face limitations related to …
  datacite Rangare, Rohini, Tiwari, Susheel Kumar 2026 置信度 0.66 Credit card fraud detection; cybersecurity; quantum cryptography; federated learning; SMOTE; ADA Boost; privacy-preserving AI
• A Review : Enhancing Cybersecurity with Quantum Cryptography and Federated SMOTE-ADA Boost Framework for Detecting Credit Card Fraud
  The rapid growth of digital financial transactions has significantly increased the risk of credit card fraud, making robust cybersecurity mechanisms essential for modern banking systems. Traditional fraud detection approaches often face limitations related to …
  datacite Rangare, Rohini, Tiwari, Susheel Kumar 2026 置信度 0.66 Credit card fraud detection; cybersecurity; quantum cryptography; federated learning; SMOTE; ADA Boost; privacy-preserving AI
• Enhancing Cybersecurity with Quantum Cryptography and Federated SMOTE-ADA Boost Framework for Detecting Credit Card Fraud
  The detection of credit card fraud has become increasingly difficult due to the sheer volume of transactions and the inherently imbalanced nature of fraud data, in which fraudulent activity accounts for only a small fraction of overall transactions. Traditiona…
  datacite Rangare, Rohini, Tiwari, Susheel Kumar 2026 置信度 0.66 Quantum Cryptography; Federated Learning; Credit Card Fraud Detection; Imbalanced Data; SMOTE-ADA Boost Framework
• Enhancing Cybersecurity with Quantum Cryptography and Federated SMOTE-ADA Boost Framework for Detecting Credit Card Fraud
  The detection of credit card fraud has become increasingly difficult due to the sheer volume of transactions and the inherently imbalanced nature of fraud data, in which fraudulent activity accounts for only a small fraction of overall transactions. Traditiona…
  datacite Rangare, Rohini, Tiwari, Susheel Kumar 2026 置信度 0.66 Quantum Cryptography; Federated Learning; Credit Card Fraud Detection; Imbalanced Data; SMOTE-ADA Boost Framework
• Federated Machine Learning for IoT/IoMT Security and Network Intelligence in HIPAA-Regulated Environments
  The increase in the deployment of Internet-of-Things (IoT) and Internet-of-Medical-Things (IoMT) devices in healthcare systems has posed unprecedented cybersecurity challenges that compromise patient safety and data integrity, on the one hand, but introduce de…
  datacite Nagappan Nagappan Palaniappan 2026 置信度 0.66
• Federated Machine Learning for IoT/IoMT Security and Network Intelligence in HIPAA-Regulated Environments
  The increase in the deployment of Internet-of-Things (IoT) and Internet-of-Medical-Things (IoMT) devices in healthcare systems has posed unprecedented cybersecurity challenges that compromise patient safety and data integrity, on the one hand, but introduce de…
  datacite Nagappan Nagappan Palaniappan 2026 置信度 0.66
• Federated Learning Architecture for Privacy-Preserving AI
  This article presents a comprehensive examination of federated learning architecture for privacy-preserving ai, addressing the critical challenges and opportunities at the intersection of architecture, advanced system architecture, and artificial intelligence.…
  datacite Ali Ahamed, Safa Mohamed 2026 置信度 0.66 Machine Learning
• Federated Learning Architecture for Privacy-Preserving AI
  This article presents a comprehensive examination of federated learning architecture for privacy-preserving ai, addressing the critical challenges and opportunities at the intersection of architecture, advanced system architecture, and artificial intelligence.…
  datacite Ali Ahamed, Safa Mohamed 2026 置信度 0.66 Machine Learning
• Sensitive-Attribute-Agnostic Fair Federated Learning Against Fairness Attacks
  datacite Guo, YiChen 2026 置信度 0.66
• Sensitive-Attribute-Agnostic Fair Federated Learning Against Fairness Attacks
  datacite Guo, YiChen 2026 置信度 0.66
• Application of Artificial Intelligence Frameworks in Development of Medical Imaging Diagnosis Systems: A Comprehensive Review of Novel Methodologies, Clinical Validation, Performance Optimization, and Future Perspectives
  Artificial intelligence (AI) has revolutionized medical imaging with automated disease detection, image segmentation, diagnosis, prognosis prediction, and clinical decision support across various imaging modalities, such as X-ray, computed tomography (CT), mag…
  datacite Susreeti Sur, Rakesh Kumar, Mandal, Debanil, Chanda 2026 置信度 0.66
• Federated Learning for Intrusion Detection in Internet of Medical Things (IoMT): A PRISMA-Based Systematic Literature Review
  Abstract The rapid expansion of Internet of Medical Things (IoMT) devices has introduced new opportunities for real-time healthcare monitoring but also increased cybersecurity risks. Traditional centralized intrusion detection systems (IDS) struggle to scale a…
  europepmc Ifeanyi Nwokoro, Edgar Osaghae, Saheed Kayode, Tombari Sibe 2026 置信度 0.80
• HashGuard-FL: Learnable Hash-Based GAN Discriminator with Post-Quantum Audit Trail for Federated Learning Integrity
  Abstract Federated learning (FL) deployments in safety-critical domains face two entangled integrity problems: (i) detecting adversarial model updates at runtime, and (ii) producing a long-lived, tamper-evident audit trail whose authenticity must survive post-…
  europepmc Nilima Dongre 2026 置信度 0.80
• DVFL-IIoT: Dynamic, Verifiable, and Decentralized Federated Learning with Key Insulation for Industrial Internet of Things
  preprints 2026 置信度 0.74
• Decoupled Text-Guided Distillation for Efficient Federated Learning on Edge Devices
  Abstract Federated Learning (FL) enables the collaborative training of models across heterogeneous edge devices while preserving data privacy; however, its performance degrades significantly under domain shift. While integrating Vision-Language Models (VLMs) c…
  preprints Younghan Kim, Yongjae Park, Jae Won Cho, Jungchan Cho 2026 置信度 0.74
• E-FLAPS: An Edge–Federated Learning Framework with Anomaly-Aware Training for Robust Stock Price Prediction
  Abstract Accurate and privacy-aware stock price prediction in distributed financial environments remains a challenging task, particularly under data heterogeneity and volatile market conditions. This paper presents E-FLAPS, a hybrid edge–Federated Learning (FL…
  europepmc MUSTAFA AL SAMARA 2026 置信度 0.80
• Physics-Informed Neural Networks for Glucose-Insulin Digital Twins Leveraging Federated Learning and IoBNT
  preprints 2026 置信度 0.74
• Zero-Knowledge Federated Learning for Privacy-Preserving 5G Authentication
  The fifth-generation (5G) networks are facing critical security challenges in device authenti- cation for massive Internet of Things deployments while preserving privacy. Traditional federated learning approaches depend on the computationally expensive homomor…
  europepmc Ahmed Lateef Salih Al-Karawi, Rafet Akdeniz 2026 置信度 0.80
• Adaptive Federated Learning for Privacy-Preserving Modeling in Heterogeneous Financial Environments
  preprints 2026 置信度 0.74
• Multi-Modal Federated Learning with Differential Privacy for Privacy-Preserving Healthcare AI
  europepmc 2026 置信度 0.80
• Standardized API Call Protocols for implementing Federated Learning in FAIRDatabase
  Abstract The rapid expansion of machine learning methodologies in biomedical research has intensified the tension between the demand for large scale data analysis and the stringent privacy regulations governing sensitive health data. The integration of federat…
  preprints Sem de Regt, Roland V. Bumbuc, Vivek M. Sheraton 2026 置信度 0.74
• The Impact of Federated Learning Maturity on Supply Chain Resilience of External Organizations: Evidence from China
  preprints 2026 置信度 0.74
• Federated Learning Performance Depends on Site Variation in Global HIV Data Consortia
  preprints 2026 置信度 0.74
• A Two-Stage Byzantine-Robust Defense Framework for Federated Learning in Distributed Non-IID Environments
  preprints 2026 置信度 0.74
• PSO-Driven Client Selection for Federated Learning in IDS applications
  Abstract Federated Learning has emerged as a highly promising distributed and collaborative learning paradigm, enabling local clients to train models without sharing their raw data. However, the global model’s performance is often impacted by heterogeneity and…
  preprints Aymen WAli, Maher Boughdiri, Hichem Mrabet, Abderrazek Jemai 2026 置信度 0.74
• FedCRM-DP: A Privacy-Preserving Federated Learning Framework for Secure Cross-Organization CRM Intelligence
  Abstract Collaborative intelligence across regulated enterprises remains constrained by stringent data-sharing restrictions imposed by frameworks such as GDPR, HIPAA, and CCPA. This paper presents FedCRM-DP, a privacy-preserving federated learning framework de…
  preprints Nikhil Donapati 2026 置信度 0.74
• FedVIB–AGP: Defending Against Distributed Backdoor Attacks in Federated Learning via Variational Information Bottleneck and Activation-Gap Pruning
  europepmc 2026 置信度 0.80
• A Heterogeneity-Aware Privacy-Preserving Federated Learning Framework Using Ensemble Clustering for Healthcare Applications
  preprints 2026 置信度 0.74
• Federated Learning-Based Intrusion DetectionFramework for Enhancing Security in Internet ofThings Environments
  Abstract The exponential expansion of the Internet of Things (IoT) has introduced substantial cybersecurity challenges, rendering interconnected devices increasingly vulnerable to cyberattacks. To address these issues, we propose a federated learning-based int…
  preprints yasmine labiod, oussama cheraita 2026 置信度 0.74
• Federated Learning: A Survey of Core Challenges, Current Methods, and Opportunities
  preprints 2026 置信度 0.74
• NeuroFLAME: A Scalable, Privacy-Preserving Federated Learning Platform for Collaborative, Secure, and Reproducible Multi-Site Neuroimaging
  europepmc 2026 置信度 0.80
• Research on Multicenter Ovarian Cancer Diagnosis Based on Federated Learning
  europepmc 2025 置信度 0.80
• TrustGraph-DFL: Byzantine-Resilient Decentralized Federated Learning via Consistency-Weighted Neighborhood Aggregation
  preprints 2026 置信度 0.74
• Adaptive network security defense method combining multi-level federated learning and adversarial training
  Abstract In order to enhance the network security defense capability in distributed environments, this paper proposes an adaptive defense method that combines multi-level federated learning and adversarial training. This method has demonstrated excellent perfo…
  preprints Yuanyuan Wang 2026 置信度 0.74
• Hybrid Transformer-Based Recommender System with LLM-Assisted Semantic Modeling for Sequential and Federated Learning
  Abstract Sequential recommender systems based on transformers have shown good performance to model the user interaction dynamics, although they usually depend mainly on the interaction data and fail to use rich semantic data that exists in item metadata. This …
  preprints Lakshmi Bai Maddala, Rajendra Pamula, Katteda Subbarao 2026 置信度 0.74
• Range-risk estimation for electric vehicles in low-coverage mountain road segments: A digital-twin study with prequential beacon-based Federated Learning
  europepmc 2026 置信度 0.80
• An Adaptive Resource-Aware MK-CKKS Framework with Linear- Complexity Multi-Key Aggregation for Privacy-Preserving Federated Learning in Resource-Constrained IoT Environments
  europepmc 2026 置信度 0.80
• Federated Learning under the Uncertainty Principle of Machine Learning
  Abstract Inspired by the fundamental insights of quantum mechanics' uncertainty principle, this paper introduces the "Uncertainty Principle of Machine Learning": an intrinsic and irreducible trade-off exists between a model's generalization capability and priv…
  preprints Zhonghui XUE, Yazheng Dang 2025 置信度 0.74
• A Multi-Layer Trustworthy Security Architecture for Smart-City IoT: Edge AI Intrusion Detection, Poisoning-Resilient Federated Learning and Ledger-Anchored Model Provenance
  europepmc 2026 置信度 0.80
• Federated Learning, Game Theory and Block Chain Hybrid for Coordination of Multi-Prosumer Grid-Connected Microgrids
  The increasing penetration of distributed renewable energy resources and electric vehicles has transformed microgrids into complex multi-prosumer systems that require coordinated control. Traditional centralized and local independent control strategies fail to…
  preprints Nicholas Nyaika 2026 置信度 0.74
• A Federated Learning-Based Distributed Solar Forecasting for Smart Buildings in Muscat, Oman Using GRU Networks
  preprints 2026 置信度 0.74
• Federated Learning over 5G/6G Networks: Dynamic Client Selection and Resource Allocation for Heterogeneous Edge Environments
  preprints 2026 置信度 0.74
• Federated Learning, Mobile Emotion Recognition, and Client-Side Data Quality: A Survey and Research Agenda
  preprints 2026 置信度 0.74
• Federated Learning-Based Cervical CancerClassification Using a Novel HybridKAN-ViT-Autoencoder Architecture
  preprints 2026 置信度 0.74
• Energy-Aware Client Selection in Hierarchical Federated Learning via Supervised and Metaheuristic Algorithms
  Hierarchical federated learning enables scalable and communication-efficient distributed training by introducing intermediate edge servers between clients and the central server. However, high energy consumption and limited network resources-such as bandwidth …
  preprints Silvana Trindade, Nelson L S Da Fonseca 2026 置信度 0.74
• Protecting Collaborative Education: A Blockchain-Powered Trust and Reward System to Reduce Poisoning Attacks in Federated Learning
  Abstract Federated Learning (FL) is a game-changing idea in education that enables institutions to create robust AI models for individualized learning while maintaining the highest level of privacy for student data. However, due to its decentralized nature, Fe…
  preprints Din Mohammad Toufik 2026 置信度 0.74
• Communication-Efficient Federated Learning for Real-Time Anti-Money-Laundering Monitoring
  preprints 2026 置信度 0.74
• Standardized API Design for Privacy-Preserving Federated Learning in FAIR-Compliant Biomedical Databases
  Abstract The rapid expansion of machine learning methodologies in biomedical research has intensified the tension between the demand for large scale data analysis and the stringent privacy regulations governing sensitive health data. The integration of federat…
  preprints Sem de Regt, Roland V. Bumbuc, Vivek M. Sheraton 2026 置信度 0.74
• FedDPGu: Adaptive Prompt-tuning with Built-in Unlearning for Federated Learning
  preprints 2025 置信度 0.74
• SGA-FL NIDS: A Similarity-Gated Asynchronous Federated Learning for Network Intrusion Detection
  preprints 2026 置信度 0.74
• FL FraDet: Federated Learning for Privacy-PreservingFraud Detection in Mobile Money Systems
  preprints 2026 置信度 0.74
• Fair Client Selection Method for Federated Learning Based on Discretized Firefly Algorithm
  Abstract Federated Learning (FL) enables collaborative model training without exchanging sensitive local data, ensuring privacy and advancing distributed machine learning. However, in edge scenarios, FL faces challenges of data heterogeneity, device resource c…
  europepmc XiaoYe Li, Yangyang Zhang, Zhenlong Sun, Wei Zhao 2025 置信度 0.80
• STellar-FL: A Decentralized Federated Learning Architecture for Scalable Cross-Institution AI Under Network-Constrained Environments
  preprints 2026 置信度 0.74
• FedGIS-Water: A Federated Learning-Enhanced GIS Framework for Circular Water Infrastructure Assessment in Railway Stations
  preprints 2026 置信度 0.74
• Adaptive Privacy-Preserving Split-Hierarchical Federated Learning for Resource-Constrained IoT Networks
  Abstract The proliferation of Internet-of-Things (IoT) devices necessitates efficient machine learning paradigms that address bandwidth constraints, privacy requirements, and computational heterogeneity. While hierarchical federated learning offers com- munica…
  preprints Yashraj Sakunde 2026 置信度 0.74
• Multi-Hospital Electronic Health Record Foundation Models Without Data Sharing: A Comparison of Federated Learning and Inference-Time Ensembling
  Foundation models for electronic health records (EHRs) perform strongly on clinical prediction, but every published model has been trained within a single health system. No multi-institutional EHR foundation model currently exists, largely because privacy regu…
  pubmed Elemento O 2026 置信度 0.82
• Adversarial attack detection in resource-constrained environments: A stable and sequential federated learning architecture with TinyLlama-1.1B
  Abstract Large Language Models (LLMs) face training challenges on resource-constrained devices, and the performance losses caused by compression methods necessitate a ‘full fine-tuning’ approach. In this study, a Mutex-based architecture is proposed for the fu…
  preprints Sevim Ceylan Böcekçi, Kazım Yıldız 2026 置信度 0.74
• Federated Learning Enhanced YOLOv8 for Privacy-Preserving Student Classroom Behavior Recognition
  Abstract In the field of smart education, real-time object detection for analyzing student classroom behaviors provides valuable, objective data to help teachers optimize teaching methods and improve student learning experiences. This supports a positive and e…
  preprints Shuai Ma, Heyou Chang, Jian Han, Hao Zheng 2025 置信度 0.74
• FedVR360: Federated Learning enabled Privacy-Preservation for VR 360° Video Streaming in Vehicular Edge Computing
  preprints 2026 置信度 0.74
• A Comprehensive Survey of Federated Learning for Edge AI: Recent Trends and Future Directions
  europepmc 2025 置信度 0.80
• Towards Federated Learning Across Biobanks: Prototype Software from the 2026 Carnegie Mellon University–NVIDIA Hackathon
  preprints 2026 置信度 0.74
• ProtoMFL: A Robust Multimodal Federated Learning Framework via Cross-Modal Prototype Integration
  Abstract Multimodal federated learning (MFL) has made substantial progress in aggregating multimodal knowledge across distributed environments. However, it still encounters persistent challenges caused by modality-missing data at the client level. Traditional …
  europepmc Junsun Zhang, Chaochao Sun, Yuan Peng 2025 置信度 0.80
• Federated Learning Approach Using Transfer Learning Architectures for Lung Cancer Detection
  There have been many advancements in the field of medical imaging but even then, accurate cancer detection remains a challenge because of limited labelled data. In the research field, a lot of work is already done for this task, using pre-trained features from…
  europepmc Purvi Choure, Shaligram Prajapat, Krishan Berwal 2025 置信度 0.80
• A Robust Federated Learning Method for Data Heterogeneity with Enhanced Momentum-guided Aggregation
  Abstract Federated learning (FL) is an emerging distributed machine learning paradigm that enables multiple edge devices to collaboratively train a model for a specific task while preserving privacy. Yet, due to the non-independent and identically distributed …
  preprints Linhai Nie, Jin Wang, naixuan Hu 2025 置信度 0.74
• SHIELD-Health: Secure Healthcare IoT with Energy-efficient Ledger-based Distributed Federated Learning
  preprints 2026 置信度 0.74
• A Robust Vehicle to Network Communication using Quantum Blockchain Cryptography and Federated Learning
  Abstract Vehicular Ad Hoc Networks (VANETs) are direct network communications established between vehicles and roadside infrastructure. This form of communication is designed to provide a seamless autonomous driving experience through constant data sharing amo…
  europepmc Koomson Robert 2025 置信度 0.80
• Distribution-Aware Federated Learning for Diabetes Prediction Using Tabular Clinical DataUnder Non-IID and Class-Imbalanced Settings
  preprints 2026 置信度 0.74
• The Convergence of Federated Learning, Knowledge Graphs, and Large Language Models for Language Learning: A Scoping Review
  Large Language Models (LLMs) in Intelligent Computer-Assisted Language Learning (iCALL) offer personalization potential but introduce critical challenges in pedagogical grounding, data privacy, and pedagogical validity. While Knowledge Graphs (KGs) and Federat…
  europepmc Michael Kenteris, Konstantinos Kotis 2026 置信度 0.80
• Blockchain and Federated Learning for Cross-Border Credential Verification: A Policy Framework for Central Asian Higher Education
  preprints 2026 置信度 0.74
• Vessel Traffic Density Prediction: A Federated Learning Approach
  preprints 2025 置信度 0.74
• Federated Learning with Smartphone Apps for Privacy-Preserving Chronic Disease Management and Cognitive Decline Detection in Seniors
  preprints 2026 置信度 0.74
• Mitigating Data Sparsity and Privacy Risks in Educational Recommender System through Federated Learning
  europepmc 2025 置信度 0.80
• SpaceTime: A Deep Similarity Defense Against Poisoning Attacks in Federated learning
  preprints 2025 置信度 0.74
• Federated Learning for Secure Data Sharing Across Distributed Networks
  Federated learning (FL) has emerged as a transformative paradigm for collaborative model training without the need to centralize sensitive information. By enabling multiple participants to train a shared model locally and only exchange model updates, FL preser…
  preprints Lawal G. Anand 2025 置信度 0.74
• Neuro-Symbolic Federated Learning with Quantum-Safe Cognitive Twins for Personality-Aware Human-AI Collaboration
  preprints 2026 置信度 0.74
• Privacy-Enhanced Federated Learning for Distributed Heterogeneous Data
  preprints 2025 置信度 0.74
• Model Poisoning Attacks to Federated Learning based on Fake Clients
  Abstract The increasing use of decentralized and anonymous networks creates vast amounts of darknet traffic, offering opportunities to enhance network security by detecting threats, filtering malicious activity, and identifying anomalies through improved traff…
  preprints Mani Ghahremani, Alan Metwally, Rahim Taheri 2025 置信度 0.74
• AP-PPFL: An Anti-poisoning Privacy-preserving Federated Learning Method
  europepmc 2025 置信度 0.80
• MMVO-SHFL: A Fair and Efficient Hierarchical Federated Learning
  Abstract Federated learning (FL) enables collaborative model training without centralizing data. However, the traditional FL framework is cloud-based and suffers from high communication latency. On the other hand, the edge-based FL framework, although reducing…
  preprints Xia Liu, Jianping Wang, Danyang Chen 2025 置信度 0.74