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• CDSA-ATM: Air Traffic Management Pillar of the CDSA Federated Reinforcement Learning Paradigm — Multi-modal Trajectory + ATS + ANSP Fusion
  CDSA-ATM is the reference implementation of the air traffic management pillar of the Complementary Diagnostic Safety Approach (CDSA), a third-generation aviation safety paradigm formalised as a Federated Reinforcement Learning (FRL) framework (Scenario C decis…
  datacite Cantekin, Mete 2026 置信度 0.66 federated reinforcement learningair traffic managementADS-BOpenSky NetworkATS occurrences
• Al in Marketing: Personalization and Recommendation Systems
  Abstract Personalization in marketing uses AI-powered recommendation systems to tailor content, products, and messages to individual users. This paper provides an analytical, rigorous survey of AI personalization, covering definitions and scope; historical evo…
  datacite Shikha Tiwari, Shyam Kumar Singh 2026 置信度 0.66
• Al in Marketing: Personalization and Recommendation Systems
  Abstract Personalization in marketing uses AI-powered recommendation systems to tailor content, products, and messages to individual users. This paper provides an analytical, rigorous survey of AI personalization, covering definitions and scope; historical evo…
  datacite Shikha Tiwari, Shyam Kumar Singh 2026 置信度 0.66
• AI-Driven Intrusion Detection for the Internet of Things: A Scoping Review of Federated Learning, Privacy-Preserving Architectures, and Edge Deployability
  Federated learning has emerged as the dominant architectural response to the privacy and communication constraints of centralised intrusion detection in Internet of Things environments, yet the field lacks a synthesis that maps the concurrent state of architec…
  datacite Gilbert Imuetinyan Osaze Aimufua, Godwin Agbonkhese 2026 置信度 0.66
• AI-Driven Intrusion Detection for the Internet of Things: A Scoping Review of Federated Learning, Privacy-Preserving Architectures, and Edge Deployability
  Federated learning has emerged as the dominant architectural response to the privacy and communication constraints of centralised intrusion detection in Internet of Things environments, yet the field lacks a synthesis that maps the concurrent state of architec…
  datacite Gilbert Imuetinyan Osaze Aimufua, Godwin Agbonkhese 2026 置信度 0.66
• jiayunz/OrthoFL: Initial Release
  Code for KDD 2026 paper: "Taming Update Drift in Asynchronous Federated Learning via Orthogonal Calibration"
  datacite Jiayun Zhang 2026 置信度 0.66
• jiayunz/OrthoFL: Initial Release
  Code for KDD 2026 paper: "Taming Update Drift in Asynchronous Federated Learning via Orthogonal Calibration"
  datacite Jiayun Zhang 2026 置信度 0.66
• An Inclusive and Lightweight Approach to Federated Continual Learning for Cultural Heritage
  Artificial intelligence can support cultural heritage and digital humanities through large-scale retrieval and analysis of digitized collections. However, cultural heritage data are often distributed across institutions, constrained by ownership and access res…
  datacite Theologitis, Ioannis, Meng, Debin, Eleftheriadis, Stylianos, Lolis, Vasileios 等 2026 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
• Enhancing Privacy in Federated Learning via Dual Obfuscation of Gradients and Training Images
  Federated learning enables collaborative model training while keeping data locally at each client; however, recent studies have shown that training data can be reconstructed from shared model updates. To address this issue, this paper proposes a dual obfuscati…
  datacite Itabashi, Yuki, Sawada, Hiroto, Hirose, Mare, Imaizumi, Shoko 等 2026 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciences
• Differential Privacy in Feature Reconstruction Aided Federated Learning for Agent's Semantic Communication Model Update
  This paper proposes a differentially private federated learning (FL) framework built upon an FL algorithm with semantic feature reconstruction (FedSFR) for training semantic communication modules for image transmission. By allowing clients with unfavorable upl…
  datacite Huh, Yoon, Kim, Bumjun, Choi, Wan 2026 置信度 0.66 Signal Processing (eess.SP)FOS: Electrical engineering, electronic engineering, information engineering
• Machine Learning Techniques for 5G Network Optimization and Resource Management: A Comprehensive Survey
  The fifth generation (5G) of mobile networks represents a fundamental paradigm shift in wireless communications, promising ultra-low latency, massive device connectivity, and peak data rates exceeding 10 Gbps. However, the unprecedented complexity of 5G networ…
  datacite Dash, Bhabani Sankar 2026 置信度 0.66 5G Networks, Machine Learning, Deep Learning, Network Optimization, Resource Management, Reinforcement Learning, Network Slicing, Federated Learning
• Machine Learning Techniques for 5G Network Optimization and Resource Management: A Comprehensive Survey
  The fifth generation (5G) of mobile networks represents a fundamental paradigm shift in wireless communications, promising ultra-low latency, massive device connectivity, and peak data rates exceeding 10 Gbps. However, the unprecedented complexity of 5G networ…
  datacite Dash, Bhabani Sankar 2026 置信度 0.66 5G Networks, Machine Learning, Deep Learning, Network Optimization, Resource Management, Reinforcement Learning, Network Slicing, Federated Learning
• Machine Learning Techniques for 5G Network Optimization and Resource Management: A Comprehensive Survey
  The fifth generation (5G) of mobile networks represents a fundamental paradigm shift in wireless communications, promising ultra-low latency, massive device connectivity, and peak data rates exceeding 10 Gbps. However, the unprecedented complexity of 5G networ…
  datacite Dash, Bhabani Sankar 2026 置信度 0.66 5G Networks, Machine Learning, Deep Learning, Network Optimization, Resource Management, Reinforcement Learning, Network Slicing, Federated Learning
• GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networks
  Deploying state-of-the-art deep neural networks (DNNs) at the wireless edge is severely bottlenecked by the strict energy and resource constraints of mobile devices. Although federated split learning (FSL) alleviates on-device computational burdens by offloadi…
  datacite Roth, Idan, Lampe, Lutz 2026 置信度 0.66 Machine Learning (cs.LG)Distributed, Parallel, and Cluster Computing (cs.DC)Signal Processing (eess.SP)FOS: Computer and information sciencesFOS: Electrical engineering, electronic engineering, information engineering
• FedGuard-DC: Privacy-Preserving Federated Load Forecasting and Cyber-Attack Detection for Data-Center Loads in Transmission Systems
  The rapid growth of large data-center (DC) loads is creating new challenges for power-system visibility, privacy, and cyber-physical security. System operators need accurate short-term information about these fast-varying loads, while DC operators may avoid sh…
  datacite Saroare, Md Kibria, Ahmed, Md Rubel 2026 置信度 0.66 Cryptography and Security (cs.CR)FOS: Computer and information sciences
• FedCoRe: Target-Adaptive Completion for Missing Modalities in Healthcare Federated Learning
  Federated multimodal models often assume every site has every modality, although hospitals differ in access to EHRs, chest radiographs, and ECGs. We study this setting on a MIMIC-derived respiratory deterioration task with simulated FL clients and introduce Fe…
  datacite Roth, Holger R., Xu, Ziyue, Cnudde, Peter 2026 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)FOS: Computer and information sciences
• Fulcrum: Topology-Aware Differential Privacy in Hierarchical Federated Learning
  @misc{rangwala2026topologyawaredifferentialprivacyfederated, title={Topology-Aware Differential Privacy in Hierarchical Federated Learning}, author={Murtaza Rangwala and Richard O. Sinnott and Rajkumar Buyya}, year={2026}, eprint={2506.19260}, archivePrefix={a…
  datacite Murtaza Rangwala 2026 置信度 0.66
• Analysis code and derived dataset for: Federated learning reconciles data sovereignty with collaborative energy-transition modelling across the Global South
  Analysis code, derived dataset and outputs for the manuscript ‘Federated learning reconciles data sovereignty with collaborative energy-transition modelling across the Global South: a public-data benchmark anchored in Indonesia’ (submitted to npj Climate Actio…
  datacite Darmanto, Sumartono 2026 置信度 0.66 federated learning; energy transition; data sovereignty; Global South; Indonesia; renewable energy
• Analysis code and derived dataset for: Federated learning reconciles data sovereignty with collaborative energy-transition modelling across the Global South
  Analysis code, derived dataset and outputs for the manuscript ‘Federated learning reconciles data sovereignty with collaborative energy-transition modelling across the Global South: a public-data benchmark anchored in Indonesia’ (submitted to npj Climate Actio…
  datacite Darmanto, Sumartono 2026 置信度 0.66 federated learning; energy transition; data sovereignty; Global South; Indonesia; renewable energy
• HeteRo-Select: Informativeness as the Participation Driver in Heterogeneous Federated Learning
  Federated learning systems typically allocate gradient compression by link speed. This is sensible when bandwidth and data informativeness align. However, under non-IID data, these signals often decorrelate or invert. A bandwidth-driven allocator then risks co…
  datacite Masud, Md. Akmol, Jahin, Md Abrar, Hasan, Mahmud 2025 置信度 0.66 Machine Learning (cs.LG)FOS: Computer and information sciences
• Dynamic Entanglement-Weighted Pruning for Quantum Federated Unlearning in Supply-Chain Risk Prediction
  Federated deployments of variational quantum classifiers are attractive for cross-organisation risk prediction in supply chains, because raw data never leaves the client, yet data-protection regulations such as the GDPR grant clients a right to request that th…
  datacite Kumar, Aditya, Chongder, Sumit 2026 置信度 0.66 Quantum Physics (quant-ph)Machine Learning (cs.LG)FOS: Physical sciencesFOS: Computer and information sciencesI.2.6; I.2.11; C.2.4
• Advances in Interdisciplinary Systems & Computing
  The rapid evolution of artificial intelligence, secure computing, distributed systems, and human-centered technologies is reshaping the way societies create, manage, and utilize digital knowledge. Contemporary research increasingly transcends traditional disci…
  datacite Chakraborty, Mohuya, Chakraborty, Jayanta 2026 置信度 0.66 InterdisciplinaryComputingAI
• Advances in Interdisciplinary Systems & Computing
  The rapid evolution of artificial intelligence, secure computing, distributed systems, and human-centered technologies is reshaping the way societies create, manage, and utilize digital knowledge. Contemporary research increasingly transcends traditional disci…
  datacite Chakraborty, Mohuya, Chakraborty, Jayanta 2026 置信度 0.66 InterdisciplinaryComputingAI
• Electromagnetically Consistent Federated RIS Channel Estimation with Component-Level Full-Wave Validation
  preprints 2026 置信度 0.74
• Federated Autonomous Cyber Agents for Privacy-Preserving Intrusion Detection in Distributed Smart Grids
  Abstract Distributed smart grids increasingly rely on interconnected cyber-physical infrastructures, edge intelligence, microgrids, distributed energy resources, and real-time demand-response mechanisms. Although this integration improves flexibility and opera…
  preprints 2026 置信度 0.74
• FedMARL-LTI: Federated Multi-Agent Reinforcement Learning with LLM-Driven Threat Intelligence for Cooperative Cyber Defense
  Cross-organization cyber defense must reconcile collaborative learning with privacy and adversarial robustness — yet standard federated learning ships full gradient tensors, leaking sensitive posture and inviting Byzantine manipulation. We present FedMARL-LTI,…
  preprints 2026 置信度 0.74
• Privacy-Preserving Federated Distillation Resilient to Client Disconnections and Poisoning Attacks
  preprints 2026 置信度 0.74
• Structure Learning in Bayesian Networks: A Systematic Pedagogical Survey
  preprints 2026 置信度 0.74
• Multi-Center Federated Clinical-Deep Learning Fusion Model for Preoperative Differentiation of Solitary Pulmonary Solid Nodules
  preprints 2026 置信度 0.74
• FedMAPPO: Federated Multi-Agent Proximal Policy Optimization for Energy-Efficient Task Offloading in Industrial IoT Networks
  preprints 2026 置信度 0.74
• Explainable Federated Multimodal Deep Learning Framework for Early Alzheimer’s Disease Detection: Integrating MRI, Clinical Data, and Expert-Guided Few-Shot Learning with Privacy-Preserving Cross-Site Validation
  preprints 2026 置信度 0.74
• A Privacy-Aware and Communication-Efficient Federated Spam Detection Framework for Multi-Cloud Environments
  europepmc 2026 置信度 0.80
• FedDep-GT-LLM: Federated Graph Transformer and Clinical Language Modeling for Multimodal Depression Analysis
  preprints 2026 置信度 0.74
• From Instrumented Towers to Vulnerable Blocks: A Physics-Informed Federated Edge Digital-Twin Framework for Translational Seismic Damage Assessment in Andean Emerging Economies (NSET-Andes)
  preprints 2026 置信度 0.74
• Contribution-Aware Federated Edge Learning for Robust Resource Allocation in Massive IoT Networks
  Abstract In the scene of large-scale Internet of Things (mIoT), the communication between high-density devices (D2D) will introduce serious co-frequency interference and cross-layer interference. Although the existing multi-agent deep reinforcement learning (M…
  preprints Hui Dun, Aowei Liu, Eryang Huan, Zhiyong Niu 2026 置信度 0.74
• Federated Fake News Detection Via Global Self-Attention and Inverse Variance Aggregation Under Data Heterogeneity
  preprints 2026 置信度 0.74
• A Privacy-Preserving Federated Spatiotemporal Dynamic Graph Neural Network Framework for Epileptic Seizure Prediction
  preprints 2026 置信度 0.74
• Research on Federated Multimodal Human Morphology Recognition Integrating Convolutional and Graph Convolutional Networks
  preprints 2026 置信度 0.74
• Reliability-Aware Coordination for Federated Predictive Maintenance Under Fixed Thresholds
  preprints 2026 置信度 0.74
• TITAN: Three-Tier Intrusion-Detection Tree-Based Adaptive Network for Industrial Internet of Things 
  preprints 2026 置信度 0.74
• Towards Transparent Rare Disease Monitoring: Federated Rule Extraction for Online Streaming NPC1 Data
  preprints 2026 置信度 0.74
• Federated foundation models for accurate disease detection
  preprints 2026 置信度 0.74
• Federated Temporal Kolmogorov-Arnold Networks for Private Low-Power Wearable Health Monitoring
  preprints 2026 置信度 0.74
• PRISM-VFL: A Differentially Private Vertical Federated Framework for Heterogeneous Multi-Task Clinical Prediction
  preprints 2026 置信度 0.74
• FedHeRM: Federated Heterogeneity-Aware Reinforcement Learning for Secure NOMA Resource Management in Vehicular Edge Networks
  The rapid expansion of 5G/6G technologies and the burgeoning demand for low-latency, high-reliability services at the network edge challenge traditional centralized cloud architectures. Edge computing offers a promising solution, yet its dynamic, resource-cons…
  preprints 2026 置信度 0.74
• WITHDRAWN: SFDP: Adaptive Noise and Dual-Weighted Aggregation for Privacy-Preserving Federated Vehicle Trajectory Prediction
  preprints 2026 置信度 0.74
• Privacy-Preserving Topic-wise Sentiment Analysis of the Iran–Israel–USA Conflict Using Federated Transformer Models
  preprints 2026 置信度 0.74
• A sustainable platform for federated health data access, AI innovation, and regulatory acceptance in alignment with the European Health Data Space principles
  The IDERHA ( I ntegration of Heterogeneous D ata and E vidence towards R egulatory and H TA A cceptance) project aims to enhance medical research by establishing one of Europe’s first pan-European, disease-agnostic health data spaces. Aligned with the European…
  preprints 2026 置信度 0.74
• Deep Learning Technologies for Real-Time Personalization in Mobile Applications
  preprints 2026 置信度 0.74
• DL-HIDS-IoT: A Lightweight Deep Learning-Enhanced Hybrid Intrusion Detection System for IoT and Wireless Sensor Networks
  preprints 2026 置信度 0.74
• Federated Edge AI for Anomaly Detection in Industrial IoT
  preprints 2026 置信度 0.74
• Representation-stable federated fault diagnostics for electric drivetrains under nonstationary vibration signals
  preprints 2026 置信度 0.74
• FTAD-Net: Federated Transfer-Adversarial Learning for Robust Breast Cancer Detection
  Abstract Breast cancer remains the second leading cause of cancer-related mortality among women worldwide, making early and accurate diagnosis pivotal for improving patient survival rates. Although deep learning (DL) models have demonstrated remarkable potenti…
  preprints Atta Ur Rahman, Mahmood Alam, Bibi Saqia, Zahid Halim 等 2026 置信度 0.74
• Secure Federated Intrusion Detection for Resource-Constrained IoT Devices Using Lightweight Cryptography: A Hardware-Validated Study
  preprints 2026 置信度 0.74
• AI Native Manufacturing Operating System for Autonomous Smart Factories Using Digital Twins, Multi Agent Artificial Intelligence, Physics Informed Machine Learning, and Reinforcement Learning.
  preprints 2026 置信度 0.74
• Trust-Governed Agentic AI for Cyber-Resilient Digital Markets: Blockchain-Audited Federated Intelligence Under Uncertainty
  preprints 2026 置信度 0.74
• Federated Multi-Agent Deep Reinforcement Learning for Joint Channel Selection and Power Control in Cognitive Radio Networks
  preprints 2026 置信度 0.74
• TrustFedKG-Health: Explainability-Aware Personalized Federated Knowledge Graph Neural Network with Uncertainty-Guided Clinical Reasoning
  preprints 2026 置信度 0.74
• Rafale: Towards the Efficient Resource-Aware Federated Knowledge Distillation on Heterogeneous Clients
  preprints 2026 置信度 0.74
• Learning Category-Aware Sensitivity for Federated Test-Time Adaptation
  Abstract Federated test-time adaptation (FTTA) addresses the challenge of adapting to unlabeled target data under distribution shifts while preserving data privacy. However, existing FTTA methods often overlook the category-aware sensitivity of samples in both…
  preprints Yingfa Zhang, Xiaohui Deng, Guangguang Yang, Serestina Viriri 等 2026 置信度 0.74
• Adaptive Reinforcement Learning Offloading: Unifying Federated Dissimilarity Measures and Generalizable Multi-Objective Optimization for Mobile Edge Computing
  preprints 2026 置信度 0.74
• RankBridge: Privacy-Preserving Rank-Based Explanation Clustering for Heterogeneous Federated Phishing Detection
  preprints 2026 置信度 0.74
• Misbehavior Detection in Internet of Vehicles: A Multi-Dimensional Survey
  preprints 2026 置信度 0.74
• AI-Mediated Continuous Assessment Infrastructure: Rethinking Educational Measurement across Contexts and Time
  preprints 2026 置信度 0.74
• Edge-Based ADL Recognition Using Room-Specialized Mixture-of-Experts
  preprints 2026 置信度 0.74
• The Impact of Innovation on the Performance and Safety of Medical Electronic Devices
  The rapid transition to digital medicine has transformed isolated electronic medical devices into intelligent, interconnected ecosystems capable of predictive analytics and continuous monitoring. The expansion of hyperconnectivity introduces significant vulner…
  preprints 2026 置信度 0.74
• Non-Monotonic Lattice Meets Cross-Chain FL: CrossFL-BCD with MC-SC for Conditional Declassification
  preprints 2026 置信度 0.74
• Federated Privacy-Preserving Multi-Modal Deep Learning for Breast Cancer Diagnosis: A Physics-Aware Approach
  preprints 2026 置信度 0.74
• AM-RAF: An Agentic, Retrieval-Augmented, Uncertainty-Gated Framework for Trustworthy Clinical Decision Support in Resource-Constrained Health Systems 
  preprints 2026 置信度 0.74
• Federated Contrastive Representation Learning for IoT Anomaly Detection Under Heterogeneous Data
  preprints 2026 置信度 0.74
• Predictive single cell foundation model for gene regulation and aging with privacy-preserving tabular learning
  preprints 2026 置信度 0.74
• DLNDD: An Explainable Deep Learning Framework for the Early Detection
  preprints 2026 置信度 0.74
• From Fragmented Evidence to a Unified GCC Demyelinating Disease Registry: A Strategic Framework for MS, NMOSD, MOGAD, Optic Neuritis, LETM, and Pediatric Acquired Demyelination
  preprints 2026 置信度 0.74
• F-DRL: Federated Dynamics Representation Learning for Robust Multi-Task Reinforcement Learning
  Reinforcement learning for robotic manipulation is often limited by poor sample efficiency and unstable training dynamics, challenges that are further amplified in federated settings due to data privacy constraints and task heterogeneity. To address these issu…
  preprints Anurag Upadhyay, Yashar Baradaranshokouhi, Xin Lu, Jun Li 等 2026 置信度 0.74
• Design and Analysis of an Integrated AI-Driven andBlockchain-Enabled Connected Framework forNext-Generation Oncology Applications
  preprints 2026 置信度 0.74
• FedGNN-SFD: A Lightweight Federated Graph Neural Network for Multi-Sensor Bearing Fault Diagnosis in Industrial IoT Systems
  preprints 2026 置信度 0.74
• Secure Medical Image Cryptanalysis with Quantum Neural Networks for IoT-Enabled Cloud Storage
  preprints 2026 置信度 0.74
• FusionNet Lite: A Lightweight Federated Deep Learning Model for Privacy-Preserving Predictive Maintenance
  Abstract Federated learning (FL) offers a privacy-preserving strategy for industrial predictive maintenance (PdM), yet many existing models remain too large or energy-intensive for deployment on edge devices. This study proposes \textit{FusionNet Lite}, an ult…
  preprints Aman Sharma, Kwan Yong Sim, Sivachandran Chandrasekaran 2026 置信度 0.74
• A Practical and Reproducible DDoS Detection Benchmark with DNS Amplification and Mixed Attacks
  preprints 2026 置信度 0.74
• FL-SNNIDS: Federated Spiking Neural Networks for Energy-Efficient Intrusion Detection in Agricultural SDN-IoT Networks
  europepmc 2026 置信度 0.80
• Adaptive geometric-attention network for two-stage lung nodule segmentation and malignancy classification in federated healthcare IoT edge environments.
  Accurate segmentation and classification of lung nodules in computed tomography (CT) scans remains a critical challenge in early lung cancer detection within distributed healthcare Internet of Things (IoT) environments. This paper presents a novel two-stage fr…
  pubmed Sufyan M, Qian J, Li J, Imran A 等 2026 置信度 0.82
• Adaptive Data Partitioning for Energy-Efficient Federated and Distributed Learning on Heterogeneous Systems
  europepmc 2026 置信度 0.80
• Federated Model Optimization for Real-Time Big Data Analytics in Smart Drug Delivery Edge Devices
  europepmc 2026 置信度 0.80
• A Question of Benchmarking. Insights from Decentralized Networks for the Governance of Federated Machine Learning.
  pubmed Guenzburger E, Hummel P 2026 置信度 0.82
• Federated parameter-free DBSCAN clustering and its application in image recognition.
  DBSCAN (A Density-Based Algorithm for Discovering Clusters in Spatial Databases with Noise) is a classic clustering algorithm. However, clustering distributed data with privacy protection in edge computing environments is a key challenge for DBSCAN. In this re…
  pubmed Cheng F, Deng Z, Alobaedy MM, Huang X 2026 置信度 0.82
• Surgery 4.0: From the Smart Operating Room to the Learning Operating Room.
  The connected operating room captures, transmits, and displays data, but it does not learn. This perspective presents Chirurgie 4.0 (C40), a French-led initiative born within the Commission Innovation of the Académie nationale de chirurgie, which proposes…
  pubmed Gumbs AA, Croner R, Couffinhal JC 2026 置信度 0.82
• BlockFedMed: A blockchain-federated learning framework for privacy-preserving mortality prediction across heterogeneous intensive care units.
  Electronic health records are distributed across different hospitals that work on powerful AI models but cannot be shared due to HIPAA and GDPR regulations. Federated learning (FL) avoids raw data sharing, yet lacks tamper-evident consent governance, adversari…
  pubmed Yadav AK, Deshmukh M 2026 置信度 0.82
• A federated attention-based stacked LSTM framework for interpretable malaria diagnosis under simulated non-IID federated conditions.
  Malaria remains a major global health burden, particularly in low-resource regions where microscopic diagnosis relies heavily on expert interpretation and is prone to variability. Although deep learning models have demonstrated strong classification performanc…
  pubmed Murugan S, Thirunadanasikamani K, Napa KK, Allasi HL 等 2026 置信度 0.82
• A Hybrid Quantum Resistant Secure Framework for Federated Healthcare Cyber-Physical Systems
  europepmc 2026 置信度 0.80
• OmniGuard V2X: A Hybrid-Security Prototype Framework with Assumption-Aware Validation for Smart Vehicle Systems
  europepmc 2026 置信度 0.80
• FedPDM-Net: a federated prototype-guided disentangled deep learning framework for explainable malaria detection.
  Malaria remains a critical global health concern, particularly in resource-limited settings where accurate and timely diagnosis is essential. While deep learning methods have improved automated detection from blood smear images, most rely on centralized traini…
  pubmed Napa KK, Murugan S, Murugan JS, Assegie TA 等 2026 置信度 0.82
• A Comprehensive Survey of Federated Model Evolution in Open Environments
  europepmc 2026 置信度 0.80
• Federated graph learning with spatio-temporal dynamics for cross-border recommendation.
  Cross-border data sharing is strictly constrained by privacy regulations, which presents a critical challenge for recommendation systems due to the severe shortage of training data. Existing federated graph neural network methods predominantly rely on the fede…
  pubmed Tan Z, Wang Y, Zheng J, Zhang N 等 2026 置信度 0.82
• TrustFedKG-Health: Explainability-Aware Personalized Federated Knowledge Graph Neural Network with Uncertainty-Guided Clinical Reasoning
  europepmc 2026 置信度 0.80
• Closed-Loop Fairness and Privacy Optimization for Federated Text Classification under Non-IID Data
  europepmc 2026 置信度 0.80
• FLMMIF: privacy-preserving federated multi-modal medical image fusion.
  As a pivotal technique in smart healthcare, medical image fusion integrates complementary functional and structural information to facilitate accurate diagnosis and enhance clinical decision-making reliability. However, existing centralized methods typically r…
  pubmed Meng L, Ren S, Wang J, Che Y 2026 置信度 0.82
• Federated generative adversarial network with hybrid transformer-GRU and explainable AI for financial fraud detection.
  Fraud detection in financial transactions has emerged as a significant issue in today's digital payment system with the surge of online banking, mobile payment, and credit card payments. Growing transaction numbers, sophisticated attack methods, extreme class …
  pubmed Juyal PK, Kolluri J, Siripuri K 2026 置信度 0.82
• Predictive fault management in smart sensor networks using a dynamic quantum-AI architecture (DynaQuAI).
  Applications in industrial and smart-infrastructure Wireless sensor networks (WSNs) in the field are increasingly expected to employ predictive intelligence, which, even when operating under dynamic configurations, heterogeneity of hardware, and strong privacy…
  pubmed Alharbi A 2026 置信度 0.82
• Federated Deep Learning-Based Zero-Trust Architecture for Intelligent Intrusion Detection in Multi-Cloud Systems
  The rapid adoption of multi-cloud environments has transformed modern digital infrastructures by improving scalability, flexibility, and service availability. However, the distributed nature of multi-cloud systems introduces significant cybersecurity challenge…
  europepmc muhammad Abubakar 2026 置信度 0.80
• FL-SDGIN: A federated graph learning approach for schizophrenia diagnosis integrating static and dynamic brain functional networks.
  Recent magnetic resonance imaging (MRI) studies have revealed connectivity abnormalities in brain networks of schizophrenia (SZ). Graph Neural Networks (GNN) through their powerful graph embedding ability provide novel approaches for brain network analysis in …
  pubmed Yang M, Huang J, Liu L, Yi C 等 2026 置信度 0.82