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• Federated Deep Learning System for Application of Healthcare in Pandemic Situation
  crossref Vandana, Chetna Kaushal 2024-07-29T08:04:57Z 置信度 0.70
• Federated learning frameworks and algorithms for energy-efficient IoT
  crossref Kiran Malik, Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Rajani 2025-01-20T05:27:31Z 置信度 0.70
• FedCLO: Federated Learning with Clustered Layer-Wise Communication Load Optimization
  crossref Tsung-Han Chang, Ted T. Kuo, Li-Jen Wang, Chia-Yu Lin 2025-01-21T18:22:35Z 置信度 0.70
• Federated Learning in Cybersecurity: Applications, Challenges, and Future Directions
  crossref Yamini Kannan 2024-07-15T11:59:21Z 置信度 0.70
• Federated Objective: Assessing Client Truthfulness in Federated Learning
  crossref Marco Garofalo, Alessio Catalfamo, Mario Colosi, Massimo Villari 2025-01-16T18:31:23Z 置信度 0.70
• Research Advanced in Personalized Federated Learning
  crossref Zizhuo Wang 2024-09-19T12:37:25Z 置信度 0.70
• Federated Learning with Over the Air Computation
  crossref Mingzhe Chen, Shuguang Cui 2024-02-19T05:04:40Z 置信度 0.70
• Enhancing Federated Learning Security, Scalability, and Future Incentives
  This study delves into the integration of Storj and blockchain technology within the context of federated learning (FL) and its implications for scalability and efficiency. By leveraging blockchain, we aimed to bolster security and transparency, while also add…
  crossref 2024-05-11T11:47:43Z 置信度 0.70
• Secure federated learning in the Internet of Health Things for improved patient privacy and data security
  crossref Kassim Kalinaki, Adam A. Alli, Baguma Asuman, Rufai Yusuf Zakari 2024-06-07T07:07:50Z 置信度 0.70
• FL-APU: A Software Architecture to Ease Practical Implementation of Cross-Silo Federated Learning
  crossref Fabian Stricker, José Antonio Peregrina, David Bermbach, Christian Zirpins 2025-01-21T18:22:35Z 置信度 0.70
• Trustworthy AI using Confidential Federated Learning
  The principles of security, privacy, accountability, transparency, and fairness are the cornerstones of modern AI regulations. Classic FL was designed with a strong emphasis on security and privacy, at the cost of transparency and accountability. CFL addresses…
  crossref Jinnan Guo, Peter Pietzuch, Andrew Paverd, Kapil Vaswani 2024-05-24T17:39:13Z 置信度 0.70
• FedHealth in Wearable Healthcare, Orchestrated Federated Deep Learning for Smart Healthcare
  crossref Bhupinder Singh, Christian Kaunert 2024-07-29T08:04:57Z 置信度 0.70
• Organization Committee
  crossref 2025-01-21T18:22:35Z 置信度 0.70
• Case studies and application for energy-efficient federated learning in IoT
  crossref M. Nalini, S. Jayasri, S. Nagammai, Daniel Arockiam 2025-01-20T05:27:31Z 置信度 0.70
• Technical Program
  crossref 2025-01-21T18:22:35Z 置信度 0.70
• Title Page
  crossref 2025-01-21T18:22:35Z 置信度 0.70
• Energy consumption and efficiency in federated learning (FL) for IoT
  crossref Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Kiran Malik, K. Babu 2025-01-20T05:27:31Z 置信度 0.70
• Communication efficiency in federated learning in IoT environment
  crossref Amutha Prabakar Muniyandi, L. Godlin Atlas, N. Deepa, Mahmoud Ahmad Al-Khasawneh 2025-01-20T05:27:31Z 置信度 0.70
• A Decentralized Federated Learning Using Reputation
  crossref Olive Chakraborty, Aymen Boudguiga 2024-03-01T18:44:53Z 置信度 0.70
• Federated Learning: Revolutionizing Financial Insights and Security in the Digital Age
  crossref V. O. Subramany, K. Arpitha, K. Anupama, R. Asha 2024-06-07T01:34:57Z 置信度 0.70
• A comprehensive survey on enhanced privacy techniques for federated learning in healthcare systems
  crossref Dasaradharami Reddy Kandati, Thippa Reddy Gadekallu 2025-01-13T08:38:15Z 置信度 0.70
• Foundations of Deep Learning
  crossref Sajid Ullah 2024-07-18T08:39:48Z 置信度 0.70
• Aggregating Low Rank Adapters in Federated Fine-Tuning
  crossref Evelyn Trautmann, Ian Hales, Martin F. Volk 2025-01-21T18:22:35Z 置信度 0.70
• Recent Advances in Federated Graph Learning
  crossref Tre’ R. Jeter, My T. Thai 2024-09-03T07:02:43Z 置信度 0.70
• Copyright Page
  crossref 2025-01-21T18:22:35Z 置信度 0.70
• Advancements of Credit Card Fraud Detection Based on Federated Learning
  crossref Hongwei Wang 2026-02-19T17:31:32Z 置信度 0.70
• Energy-efficient federated learning methods for IoT environment
  crossref Amutha Prabakar Muniyandi, Feslin Anish Mon, L. Godlin Atlas, Mahmoud Ahmad Al-Khasawneh 2025-01-20T05:27:31Z 置信度 0.70
• Federated Learning Drift Detection: An Empirical Study on the Impact of Concept and Data Drift
  crossref Leyla Rahimli, Feras M. Awaysheh, Sawsan Al Zubi, Sadi Alawadi 2025-01-21T18:22:35Z 置信度 0.70
• Real-Time Implementation of Improved Automatic Number Plate Recognition Using Federated Learning
  crossref M. Venkatanarayana, Syed Zahiruddin, Ahmed A. Elngar 2024-11-29T17:15:57Z 置信度 0.70
• Privacy in Federated Learning Natural Language Models
  crossref Phung Lai, C. Ariel Pinto 2024-09-03T07:02:43Z 置信度 0.70
• An Explainable and Comprehensive Federated Deep Learning in Practical Applications
  crossref Khalid Aziz, Sakshi Dua, Prabal Gupta 2024-07-29T08:04:57Z 置信度 0.70
• On Evaluation of AutoML over a Federated Learning Environment
  crossref Tiago Linhares, Silvio Gonçalves, Raimundo Lucas, Ramiro Viana 等 2025-01-21T18:22:35Z 置信度 0.70
• BACK MATTER
  crossref 2024-08-28T10:21:23Z 置信度 0.70
• Case Studies in Federated Learning for Healthcare
  This chapter presents a comprehensive exploration of federated learning's real-world applications in healthcare contexts across four diverse regions: China, India, Spain, and the United States. The authors delve into how this innovative approach addresses the …
  crossref Javier Prieto 2024-05-02T09:01:10Z 置信度 0.70
• Recent advances in federated learning for digital healthcare systems
  crossref Pooja Mohnani, Christoph Thümmler, Angelica Avila Castillo, Rasha Tolba 等 2024-06-07T07:08:22Z 置信度 0.70
• Secure data protection in federated learning for IoT
  crossref Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan, Kiran Malik, Sumit Singh Dhanda 等 2025-01-20T05:27:31Z 置信度 0.70
• Organizers and Sponsors
  crossref 2025-01-21T18:22:35Z 置信度 0.70
• FRONT MATTER
  crossref 2024-08-28T10:21:23Z 置信度 0.70
• Artificial Intelligence Techniques Based on Federated Learning in Smart Healthcare
  crossref Kanchan Naithani, Y. P. Raiwani, Shrikant Tiwari, Alok Singh Chauhan 2024-09-20T17:25:42Z 置信度 0.70
• AI Privacy and Security in Healthcare: A Systematic Literature Review.
  Artificial intelligence is expanding into telemedicine and telerehabilitation, yet significant privacy and security concerns persist.
  pubmed Dolezel D, Lalani K, Watzlaf V, Butler-Henderson K 等 2026 置信度 0.82
• Regime-Aware Federated Aggregation (RAFA) for privacy-preserving energy load forecasting across heterogeneous European grid clients
  europepmc 2026 置信度 0.80
• Deep learning for cardiovascular disease: a comprehensive review of detection and risk forecasting.
  The biggest health threat to the global population is cardiovascular disease (CVD), which afflicts almost one-third of the global population and causes considerable monetary and social losses. Risk analysis should be performed in a timely and appropriate manne…
  pubmed Ganeshan N, Magesh G 2026 置信度 0.82
• Structural impact of non-IID heterogeneity on federated behavioral anomaly detection in IoT and IoMT systems.
  The expansion of Internet of Things (IoT) and Internet of Medical Things (IoMT) infrastructures has increased the generation of multivariate sensor streams that reflect complex operational behaviors in industrial and clinical environments. Centralized anomaly …
  pubmed Robalino-Díaz J, Cabrera-Andrade A, Luján-Mora S, Villegas-Ch W 2026 置信度 0.82
• EcoStack-Pro: an adaptive federated learning framework for interpretable ESG auditing across heterogeneous industrial sectors.
  The paradigm shifts toward environmental, social, and governance (ESG) metrics has necessitated advanced auditing systems capable of analyzing complex, non-financial performance indicators. However, traditional centralized artificial intelligence (AI) models c…
  pubmed Azad MAK, Masum AKM, Rahman MA, Bhuiyan MTA 等 2026 置信度 0.82
• Advanced behavioral malware detection: a comprehensive MLOps framework with federated learning and real-time drift detection.
  This paper presents a comprehensive MLOps framework for behavioral malware detection that addresses critical challenges in generalization, collaboration, and operational resilience. We introduce three methodological contributions: (1) a formalized Leave-One-Ex…
  pubmed El-Hajj M, Zeineddine MAJ 2026 置信度 0.82
• Regime-Aware Federated Aggregation (RAFA) for privacy-preserving energy load forecasting across heterogeneous European grid clients
  europepmc 2026 置信度 0.80
• A federated multimodal deep learning framework for brain tumor classification using MRI.
  Brain tumor classification using MRI plays a critical role in early diagnosis and treatment planning. However, traditional centralized approaches require sharing sensitive medical data, which raises serious privacy concerns. Additionally, the distribution of d…
  pubmed Lakshmi Vasanthi K, Sree Darshne J, Venkatasubbu P, Ramasubramanian P 2026 置信度 0.82
• Statistical Manifold Generation-Driven Equipment Collaborative Personalized Fault Diagnosis.
  In Industrial Internet of Things (IIoT) scenarios, data privacy constraints and distribution discrepancies caused by time-varying working conditions hinder existing intelligent fault diagnosis models from maintaining robust generalization performance across di…
  pubmed Liu K, Shi Y 2026 置信度 0.82
• Federated Meta-Learning in the Time-Series Domain: a Scoping Review
  Abstract Wearables, industrial sensors, and other connected systems can create time-series data that is used in making decisions, predictions, and monitoring. Nevertheless, such information can be highly sensitive, and it involves the behaviour of the users, t…
  europepmc Suleman Qamar, Ian Zhou, Farzad Tofigh, Justin Lipman 等 2026 置信度 0.80
• Machine Learning for Intelligent and Adaptive Communication Systems: From Optimization to Emerging Paradigms.
  europepmc 2026 置信度 0.80
• U-SplitDoRA: an improved privacy-preserved U-shaped split parameter-efficient fine-tuning framework through weight decomposition for large language models.
  As large language models (LLMs) are getting bigger with respect to the parameter count, ranging from a few million to billions, methods like parameter-efficient fine-tuning (PEFT) have emerged as a crucial approach for adapting these LLMs, such as GPT, Llama, …
  pubmed Singh S, Subburaj B, Alagewaran R, Alphonse S 2026 置信度 0.82
• Federated training of spiking neural networks on edge hardware for audio processing.
  Spiking Neural Networks have caught significant attention recently for their potential for energy-efficient computation on neuromorphic hardware and their event-driven processing. Spiking Neural networks employ spike-based learning paradigms, which require spe…
  pubmed Kaimal SS, Jb A, Reka SS, Venugopal P 2026 置信度 0.82
• Federated learning with dynamic weighted aggregation for multi-crop disease detection: a hybrid CNN-transformer approach.
  Crop diseases pose a significant threat to agriculture globally, causing a loss of 220 billion dollars annually. The problem can be solved through traditional machine learning methods which run on central servers but face two main obstacles: farmers refuse to …
  pubmed Shashank R, Bhavadharini RM 2026 置信度 0.82
• From Integrated Care to Learning Systems.
  Integrated care is increasingly shaped by digital infrastructures, data governance, and AI-enabled analytics, yet the relevant literature remains fragmented across health-services research, digital health, and machine learning. This article reports a scoping r…
  pubmed Tsitiridis A, Perakis K, Antoniades A, Manias G 2026 置信度 0.82
• Privacy and personalisation: predicting Parkinson's disease severity from real-world gait with federated learning.
  europepmc 2026 置信度 0.80
• A federated learning with Large-Small Kernel Attention Network for image classification.
  europepmc 2026 置信度 0.80
• Comparing decentralized machine learning and AI clinical models to local and centralized alternatives: a systematic review.
  pubmed Diniz JM, Vasconcelos H, Rb-Silva R, Ameijeiras-Rodriguez C 等 2026 置信度 0.82
• Robust federated learning for UAV object detection: a joint self-distillation and drift compensation approach.
  The rapid advancement of unmanned aerial vehicles (UAVs) in disaster response and environmental monitoring has underscored the growing importance of real-time object detection within UAV swarm networks. However, the non-independent and identically distributed …
  pubmed Hangsun Y, Jiang C, Zhang Z, Ouyang H 等 2026 置信度 0.82
• Introduction to secure data sharing in primary care using the federated causal learning models.
  europepmc 2026 置信度 0.80
• Federated learning for fair autism spectrum disorder screening across age-heterogeneous populations.
  The detection of Autism Spectrum Disorder (ASD) remains challenging due to the heterogeneity of behavioural manifestations, limited dataset availability, and strict privacy requirements. Conventional centralized machine learning approaches often suffer from ov…
  pubmed Rekik S, Mehmood S, Berriche L 2026 置信度 0.82
• Retraction Note: Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approach.
  pubmed Almadhor A, Altalbe A, Bouazzi I, Al Hejaili A 等 2026 置信度 0.82
• FeXAI: Federated and Explainable AI for cyber threat detection in IoT-enabled smart transportation systems.
  The rapid development of smart cities, fueled by the growth of the Internet of Things (IoT) and interconnected systems, has greatly enhanced urban infrastructure, especially in transportation and energy management. However, this increased connectivity also rai…
  pubmed Gurushanker A, Rufus AJ, Columbus CC, Aravind CK 2026 置信度 0.82
• Multi-Task and Federated Learning for Breast and Lung Cancer Screening and Diagnosis: A Survey and Future Research Directions.
  Breast cancer (BrC) and lung cancer (LuC) are two forms of aggressive cancer that affect both men and women worldwide. Recently, multitask learning (MTL) and federated learning (FL) techniques have proven to be efficient in increasing the robustness of deep le…
  pubmed Ciobotaru A, Corches C, Gota D, Miclea L 2026 置信度 0.82
• TwinGuard-Sec: a federated blockchain-enabled AI framework for standardized security and privacy in cross-domain digital twin ecosystems over 6G.
  The fast rate of cross-domain Digital Twins (DT) ecosystem growth in 6G-based scenarios poses unresolved security and privacy issues into the scope of existing frameworks. This study examines the inherent constraints of existing methods and presents TwinGuard-…
  pubmed Alnfiai MM, Alotaibi RM, Alotaibi FA 2026 置信度 0.82
• Recent advances in defending the privacy attacks of large language models for healthcare applications: a concise review.
  Large language models (LLMs) are increasingly adopted across healthcare applications, including clinical decision support and medical documentation systems. However, their deployment in medical settings raises significant privacy and security concerns due to t…
  europepmc 2026 置信度 0.80
• Systematic review of trends in deep learning for UAV cybersecurity.
  Unmanned Aerial Vehicles (UAVs) operate in navigation, sensing, and communication environments that are frequently degraded or adversarial. Their attack surface spans flight-control and payload software, radio links, and swarm coordination. This PRISMA-aligned…
  pubmed Tariq U, Ahanger TA, Ahmed I 2026 置信度 0.82
• FedSMOTE-DP: Privacy-Aware Federated Ensemble Learning for Intrusion Detection in IoMT Networks.
  europepmc 2026 置信度 0.80
• RepFed-Net: privacy-preserving federated learning with reputation-aware aggregation for retinal vessel segmentation using enhanced U-Net.
  Accurate retinal blood vessel segmentation is essential for the early diagnosis of vision-threatening diseases such as diabetic retinopathy, glaucoma, and retinal vein occlusion. Although deep learning–based segmentation models have achieved promising p…
  pubmed Sri AV, Morampudi MK 2026 置信度 0.82
• Personalized multi-agent reinforcement learning framework for adaptive chronic disease therapy management.
  europepmc 2026 置信度 0.80
• Enhancing segmentation fairness through curriculum learning and progressive loss: a centralized and federated perspective on radiograph analysis.
  Bias in medical image segmentation can lead to unequal performance across demographic subgroups, raising concerns about fairness and reliability in clinical AI systems. While deep learning models have achieved high segmentation accuracy, ensuring equitable per…
  pubmed Alam EE, Littlefield N, Shaban-Nejad A, Moradi H 2026 置信度 0.82
• Computational and multi-omics systems biology for precision microbiome therapeutics.
  The human gut microbiome represents a complex and dynamic therapeutic target whose effective interrogation requires system-level analytical approaches beyond single-omics or reductive methods. This mini-review synthesizes recent advances in computational model…
  pubmed Dewan A, Mascellino MT 2026 置信度 0.82
• On Vision Transformer Explainability for Personal Protective Equipment Detection: A Qualitative and Quantitative Analysis.
  The safety of workers in industrial settings is ensured through the correct use of Personal Protective Equipment (PPE). The use of such equipment can be monitored using Deep Learning (DL). Federated Machine Learning (FML) is a technique that can be used in thi…
  pubmed Di Renzo M, Niro F, Agnello P, Petyx M 等 2026 置信度 0.82
• Cross-language hotel review sentiment analysis via multi-agent federated learning with heterogeneous graph attention networks.
  pubmed Han X 2026 置信度 0.82
• Artificial intelligence in shoulder arthroplasty: latest concepts and clinical integration.
  Recent advances in artificial intelligence (AI) have redefined shoulder arthroplasty, ultimately improving diagnostic accuracy, refining surgical planning, and personalizing recovery pathways. However, despite promising technical applications, comparative clin…
  pubmed Jeon YD, Oh J, Park KB, Jeong HJ 等 2026 置信度 0.82
• GNN-ML-FRL: a graph-enhanced meta-adaptive federated learning framework for scalable pest identification and ernvironmental modeling.
  Heterogeneous agro-ecological factors, insect breeding, and climate change are serious challenges to sustainable agricultural management. The study proposes a graph-enhanced meta-adaptive federated learning framework (GNN-ML-FRL) to address the challenges in p…
  pubmed Mahalakshmi, T S, Mathivanan SK, R S 等 2026 置信度 0.82
• MedLedgerFL: a hybrid blockchain-federated learning framework for secure remote healthcare services.
  The fast growth of telemedicine has made it clear that we need safe, cooperative, and privacy-protecting ways to handle sensitive medical information. Traditional centralised predictive model training methods have problems including data leaks, ownership dispu…
  europepmc Dileep Kumar Murala, Lavanya Vemulapalli, Yadaiah Balagoni, Eswar Patnala 等 2026 置信度 0.80 Computer scienceScalabilityFederated learningBlockchainHomomorphic encryption
• FedEmoNet: Privacy-preserving federated learning with TCN-Transformer fusion for cross-corpus speech emotion recognition.
  Federated learning offers a promising path toward privacy-preserving speech emotion recognition, yet existing approaches remain confined to single-corpus evaluation, lack formal differential privacy guarantees, and provide no mechanism for model interpretabili…
  pubmed Tawfik M, Obeidat RA, Kamel S, Aljarrah NA 等 2026 置信度 0.82
• Federated learning and Data Lakehouse for healthcare analytics: a knowledge transfer initiative between Germany and Tunisia.
  Healthcare institutions worldwide generate growing volumes of heterogeneous clinical data, yet legal, ethical, and infrastructural constraints often prevent these data from being centralized for analysis. Federated learning approaches offer a promising solutio…
  pubmed Taieb MAH, Merdassi M, Tlili A, Bouri MA 等 2026 置信度 0.82
• A federated learning framework for ethical dynamic treatment allocation across heterogeneous hospitals.
  europepmc 2026 置信度 0.80
• Federated learning-driven intelligent framework for multi-center radiotherapy dose distribution prediction oriented toward linear accelerators.
  High-quality radiotherapy dose distribution prediction for linear accelerators remains a labor-intensive process constrained by inter-planner variability and institutional data silos. While deep learning has shown promise in automating dose distribution predic…
  pubmed Zeng Y, Chen Z, Wang B, Zhang J 等 2026 置信度 0.82
• Federated spatial-temporal traffic forecasting with VMD-enhanced graph attention and LSTM.
  europepmc 2026 置信度 0.80
• Federated microservices architecture with blockchain for privacy-preserving and scalable healthcare analytics.
  europepmc 2026 置信度 0.80
• KAN-Former: a lightweight ECG model for real-time atrial fibrillation detection on wearable devices.
  europepmc 2026 置信度 0.80
• A unified low-carbon cybersecurity framework integrating energy-efficient intrusion detection, lightweight cryptography, and carbon-aware scheduling for edge-cloud architectures.
  europepmc 2026 置信度 0.80
• Federated learning for critical electrical infrastructure-handling data heterogeneity for predictive maintenance of substation equipment.
  europepmc 2025 置信度 0.80
• A Privacy-Preserving Artificial Intelligence-Driven Sensing System for Distributed Multimodal Risk Detection.
  Withthe widespread deployment of intelligent terminals, mobile payment platforms, and Internet of Things devices, security systems are being progressively transformed from traditional transaction outcome analysis toward an intelligent perception paradigm cente…
  pubmed Zhu Y, Song Y, Xuan Y, Pu J 等 2026 置信度 0.82
• A zero-trust digital twin framework for privacy-preserving multi-dataset intrusion detection in industrial IoT with lightweight blockchain auditing.
  europepmc 2026 置信度 0.80
• EnDuSecFed: an ensemble approach for privacy preserving Federated Learning with dual-security framework for sustainable healthcare.
  europepmc 2025 置信度 0.80
• Federated Learning for Histopathology Image Classification: A Systematic Review.
  pubmed Touhami M, Ahmad Fauzi MF, Ur Rehman Z, Mansor S 2026 置信度 0.82
• Privacy, Security & Governance Frameworks for AI-Powered Wearable Internet of Health Things in Elderly Care: A Comprehensive Review.
  The global aging population is expanding at an unprecedented rate, with projections indicating that 1.4 billion people will be aged 60 years or older by 2030 and 2.1 billion by 2050, placing immense pressure on healthcare systems worldwide. Artificial intellig…
  pubmed Dharmansyah D, Rahayuwati L, Pramukti I, Mutyara K 2026 置信度 0.82
• FPECGNET: A deep learning framework based on federated learning and prototype learning for interpretable ECG classification with privacy.
  Under everyday operational conditions, data privacy regulations prohibit sharing information between hospitals or institutions, contributing to the emergence of data silos. This scarcity of data hinders improvements in model performance. Another major obstacle…
  pubmed He J, Xiong J, He S, Chen Y 等 2026 置信度 0.82
• Design of an AI-driven secure 5G-SDN framework with federated reinforcement learning for anomaly detection, mitigation, and attack forensics.
  Introduction The increasing adoption of Software-Defined Networking (SDN) in 5G networks has revolutionized network management. However, this paradigm shift has introduced critical security vulnerabilities, including data-plane anomalies, control-layer intrusi…
  europepmc 2026 置信度 0.80
• A causal multimodal framework for privacy-preserving early-stage cancer detection and adaptive testing.
  europepmc 2026 置信度 0.80
• A Systematic Review of Federated and Cloud Computing Approaches for Predicting Mental Health Risks.
  pubmed Fiaz I, Kanwal N, Al-Said Ahmad A 2025 置信度 0.82
• Toward Energy-Efficient and Low-Carbon Intrusion Detection in Edge and Cloud Computing Based on GreenShield Cybersecurity Framework.
  europepmc 2026 置信度 0.80
• Improving Learning of New Diseases Through Knowledge-Enhanced Initialization for Federated Adapter Tuning.
  europepmc 2026 置信度 0.80
• A new adaptive federated learning approach for privacy preserving UAV anomaly detection under non-IID distributions.
  europepmc 2026 置信度 0.80
• FedMal-XAI: an explainable federated vision transformer leveraging knowledge distillation for privacy-preserving malaria detection.
  Plasmodium parasites are the cause of malaria, a deadly illness that continues to pose a serious danger to world health, especially in areas with low resources where subjectivity, complexity, along with privacy issues make it difficult to employ traditional di…
  pubmed Bhuiyan TA, Rahman A, Khan FI, Noori FM 等 2026 置信度 0.82
• Artificial Intelligence-Driven Multimodal Sensor Fusion for Complex Market Systems via Federated Transformer-Based Learning.
  In highly digitalized and networked modern trading systems, large volumes of heterogeneous data are continuously generated from multiple sources during market operations. However, due to the complexity of data structures, significant differences in temporal sc…
  pubmed Shi L, Tian M, Yi Y, Hu X 等 2026 置信度 0.82
• Lightweight and Energy-Aware Intrusion Detection for Industrial IoT Using TinyML and Edge AI.
  Industrial Internet of Things (IIoT) ecosystems are expanding rapidly. Scalable and reliable intrusion detection systems (IDS) are needed to protect critical infrastructures from evolving cyber threats. This study proposes a hybrid IDS framework that combines …
  pubmed Nassef L, Alghamdi MI, Chaabane SB, Abbas Q 等 2026 置信度 0.82