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• Challenges in translating AI-driven ASD/ADHD diagnosis: A methodological systematic review.
  Early and accurate diagnosis of neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), remains a critical challenge in pediatric care. Traditional methods rely on subjective behavioral…
  pubmed Rasool A, Ahmad F, Bunterngchit C, Aslam S 2026 置信度 0.82
• Deep-Fed: A comprehensive solution for precise bone fracture identification in athletes.
  europepmc 2026 置信度 0.80
• Prediction of <i>β</i>-thalassemia carrier using federated learning and explainable AI.
  europepmc 2026 置信度 0.80
• Localized AI for stroke care in LMICs: a framework to overcome structural and diagnostic barriers.
  Low- and middle-income countries (LMICs) bear a disproportionate share of the global stroke burden, driven not only by resource limitations but also by systemic inefficiencies in workforce distribution, diagnostic access, and prehospital care coordination. Whi…
  pubmed Liu Q, Jia X, He Y, Hou Y 等 2026 置信度 0.82
• Machine Learning-Based Data Extraction Tools in Healthcare: A Systematic Review.
  The healthcare industry's digital transformation has led to an unprecedented volume of multimodal data. Machine learning (ML)-based extraction tools offer promising solutions for managing this data explosion, particularly when integrated with federated databas…
  pubmed Khalpey Z, Rorvig M, Khalpey ZI, Kumar U 等 2026 置信度 0.82
• European validation of the Barcelona magnetic resonance predictive model for significant prostate cancer detection in prostate biopsies.
  europepmc 2026 置信度 0.80
• Privacy-preserving federated learning with light-weight attention improved CNNs for automated leukemia detection across distributed medical imaging.
  europepmc 2026 置信度 0.80
• Federated learning for COVID-19 mortality prediction in a multicentric sample of 21 hospitals.
  europepmc 2025 置信度 0.80
• AnomLocal: A hybrid local-global anomaly detection model for network security using federated learning.
  europepmc 2026 置信度 0.80
• A Decade of Artificial Intelligence in Stroke Care (2015-2025): Trends, Clinical Translation, and the Precision Medicine Frontier-A Narrative Review.
  Background/Objectives: Stroke generates 157 million disability-adjusted life-years (DALYs) annually, making it the leading neurological cause of global disease burden. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative techno…
  pubmed Urfy M, Mir MT 2026 置信度 0.82
• Cognitive sovereignty and decolonial public health: reclaiming epistemic authority in the global AI era.
  As artificial intelligence (AI) becomes critical infrastructure for global health, it reproduces colonial patterns of extraction, mining data from the Global South to train models owned by the Global North. While international bodies like the WHO emphasize "et…
  pubmed Kakraba S, Agyemang EF, Srivastav SK 2026 置信度 0.82
• Comparative Performance Evaluation of Federated and Centralized Learning for Velum and OTE Segmentation in Sleep Endoscopy Images.
  pubmed Yeom JC, Kim JY, Kim YJ, Kim KG 等 2026 置信度 0.82
• Event-Triggered Federated Reinforcement Broad Learning for Intelligent Fault Diagnosis in Uncrewed Helicopter Swarm Systems.
  europepmc 2025 置信度 0.80
• PANDIA: Personalized neuro-symbolic multimodal fusion for interpretable neonatal pain assessment.
  Effective pain assessment in infants aged 0-3 months is a critical challenge in neonatal intensive care units (NICUs) and family medicine clinics, where self-reporting is impossible and current observational tools remain subjective and inconsistent. This paper…
  pubmed El Othmani O, Naouali S 2026 置信度 0.82
• A Comprehensive Review of Artificial Intelligence for Brain Tumor Analysis: Taxonomy, Robustness, and Open Challenges in Neuro-Oncology.
  Detecting brain tumors can be challenging as a clinical problem because of tumor heterogeneity and reliance on manual neuroimaging interpretation, which can be prone to human error. Artificial intelligence (AI) has shown strong potential as a clinical decision…
  pubmed Qasem MH, Sariera TMA, Alhumaid K, Alshraah SM 等 2026 置信度 0.82
• Precision oncology in the age of AI: lessons from AI-driven drug discovery and clinical translation.
  Drug discovery has been constrained by extended timelines and high costs, as the cumulative requirements of preclinical validation, multi-phase clinical trials, and regulatory approval have been imposed. Recently, computational modeling has been explored as a …
  europepmc 2026 置信度 0.80
• EF-Feddr: communication-efficient federated learning with Douglas-Rachford splitting and error feedback.
  europepmc 2026 置信度 0.80
• A spatio-temporal graph diffusion and federated contrastive learning framework for cross-institutional educational evaluation.
  europepmc 2025 置信度 0.80
• Advancing Data Sovereignty in Africa: Deploying DataSHIELD to Federate Cross-Border Data Silos within the Data Science Without Borders Initiative
  Abstract Collaborative health research across Africa is constrained by data sovereignty concerns, heterogeneous regulatory frameworks, limited infrastructure, and persistent capacity gaps, which hinder equitable cross-border data sharing. These challenges limi…
  europepmc 2026 置信度 0.80
• Population health management fit lifecycles in analytics.
  "Population Health Management (PHM), Fit Lifecycles in Analytics" examines the policy and practice of AI-driven methodologies to enhance public health and patient safety in the context of the Human Phenotype Ontology (HPO). It aims for personalized healthcare …
  pubmed Henry JA 2025 置信度 0.82
• Hybrid electricity management system for residential power block applications.
  Developing countries have been facing electricity crisis for decades, mainly due to insufficient electricity generating capacity as compared to its demand and extensive growth in population. This research outlines Electric_Fed, a decentralized electricity mana…
  pubmed Tehseen R, Omer U, Hamraz AR, Mustaqeem A 等 2026 置信度 0.82
• Model centric collaboration reduces data sharing barriers in medical artificial intelligence.
  pubmed Dai Y, Cai Y, Xu Y, Cai Q 等 2026 置信度 0.82
• Explainable Federated Learning for Multi-Class Heart Disease Diagnosis via ECG Fiducial Features.
  europepmc 2025 置信度 0.80
• The role of artificial intelligence in advancing population-based cancer registration.
  europepmc 2026 置信度 0.80
• F-Transformer: a federated transformer for efficient and privacy-preserving sequence generation.
  Transformer models have demonstrated remarkable success in natural language processing (NLP) tasks, but their deployment in distributed environments faces critical challenges including high computational demands, large memory footprints, and privacy concerns w…
  pubmed Patel N, Brahmbhatt S, Ramoliya F, Vyas O 等 2026 置信度 0.82
• Perception and Prediction of Factors Influencing Carbon Price: Multisource, Spatiotemporal, Hierarchical Federated Learning Framework with Cross-Modal Feature Fusion.
  europepmc 2025 置信度 0.80
• State-of-the-art TinyML approaches for colorectal cancer detection: Current advances, challenges, and future directions.
  Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, with diagnostic disparities, particularly pronounced in resource-constrained and decentralized healthcare settings. Recent advances in TinyML machine learning models optimiz…
  pubmed Bhat SA, Chen MC, Huang NF 2026 置信度 0.82
• A comprehensive review of deep learning applications in the segmentation and classification of skin cancer.
  Skin cancer (SC) is one of the most prevalent forms of cancer worldwide. Both melanoma and non-melanoma types pose major challenges for early detection, accurate diagnosis, and proper treatment. Conventional diagnostic approaches, such as biopsy and visual exa…
  pubmed Saleh N, Shaaban AR, Salaheldin AM 2026 置信度 0.82
• Explainable and secure federated learning for privacy-enhancing skin cancer classification using a lightweight multi-scale CNN.
  europepmc 2026 置信度 0.80
• Clinical artificial intelligence (AI) in Japan: Regulatory pathways, domain-specific evidence, and its data infrastructure from an international perspective.
  europepmc 2026 置信度 0.80
• CNN-Based Fashion-MNIST Classification with Insights Toward AI-Based Mental Health Detection
  europepmc 2026 置信度 0.80
• A robust and verifiable federated learning framework for preventing data poisonous threats in e-health.
  europepmc 2026 置信度 0.80
• Quantum deep learning-enhanced ethereum blockchain for cloud security: intrusion detection, fraud prevention, and secure data migration.
  europepmc 2025 置信度 0.80
• AI and network biology for rational polypharmacology in signaling drug design: a review.
  europepmc 2026 置信度 0.80
• Secure quantum-resilient smart city communication networks using QSC-Net with MF-MBO-based energy-aware task scheduling.
  Adaptable optimisation that preserves efficiency under time-varying system dynamics necessitates modern task management in innovative city development and in distributed edge-cloud computing systems. Conservative optimisation techniques such as genetic algorit…
  europepmc 2026 置信度 0.80
• Bringing multi-modal multi-task federated foundation models to education domain: prospects and challenges.
  europepmc 2025 置信度 0.80
• Implementing federated learning for privacy-preserving emotion detection in educational environments.
  Emotion detection has become an essential tool in educational settings, where understanding and responding to students' emotions is crucial to improving their engagement, academic performance, and emotional well-being. However, traditional emotion detection sy…
  europepmc Rommel Gutiérrez, William Villegas-Ch, Sergio Luján‐Mora 2025 置信度 0.80 Computer scienceSoftware deploymentConfidentialityScalabilityData pre-processing
• A Review of Embedded Artificial Intelligence Research (2023-2026): Technological Advancements, Representative Advances, and Future Prospects.
  Since the publication of the "Review of Embedded Artificial Intelligence Research" in 2023, driven by innovations in hardware architectures, advances in lightweight algorithms, and the maturation of edge-cloud collaboration technologies, embedded artificial in…
  pubmed Zhang Z 2026 置信度 0.82
• Intelligent ship traffic supervision system based on distributed blockchain and federated reinforcement learning for collaborative decision optimization.
  europepmc 2025 置信度 0.80
• IDH Mutation Assessment in Gliomas from Anatomical MRI Using Deep Learning: A Comparative Analysis of Centralized and Federated Learning Frameworks.
  europepmc 2026 置信度 0.80
• Secure multi-party test case data generation through generative adversarial networks.
  europepmc 2026 置信度 0.80
• Privacy-preserving federated prediction of health outcomes using multi-center survey data.
  europepmc 2026 置信度 0.80
• Neural Computing Advancements in Cardiac Imaging: A Review of Deep Learning Approaches for Heart Disease Diagnosis.
  Heart disease remains a leading cause of mortality worldwide, and timely and accurate diagnosis is crucial for improving patient outcomes. Medical imaging plays a pivotal role in this process, yet traditional diagnostic methods often suffer from limitations, i…
  pubmed Berghout T 2026 置信度 0.82
• Federated Deep Learning Approaches for Detecting Ocular Diseases in Medical Imaging: A Systematic Review.
  Artificial intelligence has significantly enhanced disease diagnosis in healthcare, particularly through Deep Learning (DL) and Federated Learning (FL) approaches. These technologies have shown promise in detecting ocular diseases using medical imaging while a…
  pubmed Gulati S, Guleria K, Goyal N, Dogra A 2025 置信度 0.82
• AI-driven routing pipeline in software-defined networks using DQL: a mini review.
  pubmed Goteti D, Reddy VK 2025 置信度 0.82
• An epilepsy prediction and management system based on federated learning combined with hybrid harmony search and mutual information (HAS-MI)-based feature selection approach.
  europepmc 2025 置信度 0.80
• Confidence-calibrated federated graph attention for internet of things agents under latency SLOs.
  europepmc 2026 置信度 0.80
• Preface
  crossref 2024-02-23T09:48:21Z 置信度 0.70
• Contents
  crossref 2024-02-23T09:48:18Z 置信度 0.70
• Index
  crossref 2024-02-23T09:49:56Z 置信度 0.70
• Front Matter
  crossref 2024-02-23T09:48:20Z 置信度 0.70
• Federated Learning for Digital Healthcare Systems
  crossref 2024-06-07T12:18:47Z 置信度 0.70
• Vertical Federated Linear Regression Algorithm
  crossref 2024-08-28T10:21:23Z 置信度 0.70
• Robust Federated Learning via Weighted Median Aggregation*
  crossref Hibatallah Kabbaj, Rachid El-Azouzi, Abdellatif Kobbane 2025-01-21T18:22:35Z 置信度 0.70
• Federated Learning using TensorFlow
  crossref Rajat Patil, Deepti Gupta, H. L. Gururaj 2024-06-07T01:34:57Z 置信度 0.70
• Copyright
  crossref 2024-06-07T07:10:05Z 置信度 0.70
• Review: Recent Applications on Federated Learning
  crossref M. Spoorthi, H. L. Gururaj 2024-06-07T01:34:57Z 置信度 0.70
• Towards Efficient Belt Conveyor Maintenance: Leveraging Federated Learning
  crossref Hamza Safri, Mohamed Mehdi Kandi, Youssef Miloudi 2025-01-21T18:22:35Z 置信度 0.70
• Technical considerations of federated learning in digital healthcare systems
  crossref Emmanuel Alozie, Hawau I. Olagunju, Nasir Faruk, Salisu Garba 2024-06-07T07:09:04Z 置信度 0.70
• Global Outlier Detection in a Federated Learning Setting with Isolation Forest
  crossref Daniele Malpetti, Laura Azzimonti 2025-01-21T18:22:35Z 置信度 0.70
• Taxonomy for federated learning in digital healthcare systems
  crossref Friday Udeji, Samarendra Nath Sur, Vinoth Babu Kumaravelu, K.V.N. Kavitha 2024-06-07T07:08:37Z 置信度 0.70
• Legal implications of federated learning integration in digital healthcare systems
  crossref Agbotiname Lucky Imoize, Mohammad S. Obaidat, Houbing Herbert Song 2024-06-07T07:09:21Z 置信度 0.70
• Integration of federated learning paradigms into electronic health record systems
  crossref Hope Ikoghene Obakhena, Agbotiname Lucky Imoize, Francis Ifeanyi Anyasi 2024-06-07T07:10:02Z 置信度 0.70
• Bayesian Federated Learning with Stochastic Variational Inference
  crossref Mehreen Tahir, Feras Awaysheh, Sadi Alawadi, Muhammand Intizar Ali 2025-01-21T18:22:35Z 置信度 0.70
• Meta-Learning for Federated Face Recognition in Imbalanced Data Regimes
  crossref Arwin Gansekoele, Emiel Hess, Sandjai Bhulai 2025-01-21T18:22:35Z 置信度 0.70
• Federated learning for Supply Chain Management 5.0
  crossref Bushra Tahir, Muhammad Tariq 2025-01-13T08:38:15Z 置信度 0.70
• Collaborative Intelligence: Blockchain-Enhanced Federated Learning
  Federated Learning (FL) and blockchain technology have emerged as transformative solutions to current challenges. FL offers a decentralized machine-learning approach, prioritizing privacy, while blockchain promises transparency, security, and decentralization …
  crossref Satwik Sinha 2024-04-08T13:19:05Z 置信度 0.70
• Preface
  crossref 2024-06-07T07:07:44Z 置信度 0.70
• Towards Federated Learning-Based Forecasting of Renewable Energy Production
  crossref Viktor Walter, Fabian Stricker, Andreas Wagner, Christian Zirpins 2025-01-21T18:22:35Z 置信度 0.70
• Federated learning-enabled 5G and beyond for Industry 5.0
  crossref Chamitha De Alwis 2025-01-13T08:38:15Z 置信度 0.70
• Federated Learning Meets Network Coding: Efficient Coded Hierarchical Federated Learning
  crossref Tianli Gao, Jiahong Lin, Congduan Li, Chee Wei Tan 等 2024-12-30T19:18:57Z 置信度 0.70
• Index
  crossref 2024-06-07T07:10:19Z 置信度 0.70
• Contents
  crossref 2024-06-07T07:10:16Z 置信度 0.70
• Client-Side Adaptation to Concept Drift in Federated Learning
  crossref Finn Saile, Julius Thomas, Dominik Kaaser, Stefan Schulte 2025-01-21T13:22:35Z 置信度 0.70
• Synthetic Monoclass Teachers Distillation in the Edge Using Federated Learning Approach
  crossref Cédric Maron, Virginie Fresse, Avigaël Ohayon 2025-01-21T18:22:35Z 置信度 0.70
• Opportunities and Challenges in Federated Learning
  crossref Shiv Bhargava, Deepti Gupta, H. L. Gururaj 2024-06-07T01:34:57Z 置信度 0.70
• Deployment of Federated Learning on a Low-Cost Distributed Infrastructure
  crossref Víctor Hidalgo Izquierdo, Carmen Carrión, Blanca Caminero 2025-01-21T18:22:35Z 置信度 0.70
• Case studies and recommendations for designing federated learning models for digital healthcare systems
  crossref Chun-Ying Wu, Pushpanjali Gupta, Sulagna Mohapatra 2024-06-07T07:08:32Z 置信度 0.70
• Balancing Privacy and Performance for Private Federated Learning Algorithms
  crossref Xiangjian Hou, Sarit Khirirat, Mohammad Yaqub, Samuel Horváth 2025-01-21T18:22:35Z 置信度 0.70
• A Review of Split Learning and Federated Learning: Challenges and Synergies
  Split Learning and Federated Learning have emerged as key techniques in the domain of privacy-preserving distributed machine learning. This paper reviews the recent developments in both paradigms, discussing their respective advantages, limitations, and the po…
  crossref Nneka Obi 2024-08-19T12:34:08Z 置信度 0.70
• FEDn – A Scalable Federated Machine Learning Framework for Cross-Device and Cross-Silo Environments
  crossref Fredrik Wrede, Viktor Valadi 2025-01-21T18:22:35Z 置信度 0.70
• Efficient Federated Learning on Resource-Constrained Edge Devices: Integrating Data Distillation and Semi-Supervised Learning
  crossref Mahdi Barhoush, Ahmad Ayad, Mohammad Kohankhaki, Anke Schmeink 2025-01-21T18:22:35Z 置信度 0.70
• Data Skew in Federated Learning: An Experimental Evaluation on Aggregation Algorithms
  crossref Leon Nascimento, Feras M. Awaysheh, Sadi Alawadi 2025-01-21T18:22:35Z 置信度 0.70
• Bridging AI and Privacy: Federated Learning for Leukemia Diagnosis
  crossref Chaima Lhasnaoui, Addi Ait-Mlouk, Tarik Agouti, Mohammed Sadgal 2025-01-21T18:22:35Z 置信度 0.70
• Exploring Federated Learning Dynamics for Black-and-White-Box DNN Traitor Tracing
  crossref Elena Rodríguez-Lois, Fernando Pérez-González 2025-01-21T18:22:35Z 置信度 0.70
• Federated Learning in Secure and Reliable Systems for IoVs
  The Internet of Vehicles (IoV) is an emerging technology that allows vehicles to communicate with each other and with the infrastructure around them. This technology has the potential to revolutionize the transportation industry, but it also raises concerns ab…
  crossref Umang Kant, Prachi Dahiya 2024-12-19T05:23:34Z 置信度 0.70
• Heterogeneous SplitFed: Federated Learning with Trainable and Untrainable Clients
  crossref Juliana N. D. da Silva, Stefan Duffner, Virginie Fresse 2025-01-21T18:22:35Z 置信度 0.70
• Federated learning challenges and risks in modern digital healthcare systems
  crossref Kassim Kalinaki, Owais Ahmed Malik, Umar Yahya, Daphne Teck Ching Lai 2024-06-07T07:08:02Z 置信度 0.70
• Federated Learning in Secure and Reliable Systems for IoVs
  The Internet of Vehicles (IoV) is an emerging technology that allows vehicles to communicate with each other and with the infrastructure around them. This technology has the potential to revolutionize the transportation industry, but it also raises concerns ab…
  crossref Umang Kant, Prachi Dahiya 2024-12-26T08:01:16Z 置信度 0.70
• Future Directions and Advances in Federated Learning
  crossref Ribhav Yadav, Deepti Gupta, H. L. Gururaj 2024-06-07T01:34:57Z 置信度 0.70
• Performance evaluation of federated learning algorithms using breast cancer dataset
  crossref Sakinat Oluwabukonla Folorunso, Joseph Bamidele Awotunde, Abdullahi Abubakar Kawu, Oluwatobi Banjo 2024-06-07T07:09:30Z 置信度 0.70
• Blockchain-based federated learning for Industry 5.0 applications
  crossref Rukhsana Ruby, Zehua Wang, Lu Wang 2025-01-13T08:38:15Z 置信度 0.70
• 2024 2nd International Conference on Federated Learning Technologies and Applications (FLTA)
  crossref 2025-01-21T18:23:47Z 置信度 0.70
• Title page
  crossref 2024-06-07T07:09:11Z 置信度 0.70
• Federated Learning
  crossref 2024-11-08T00:28:12Z 置信度 0.70
• Federated Learning for Multi-Institutional AI in Healthcare via Digital Pathology
  preprints 2025 置信度 0.74
• Lightweight Federated Learning with Genetic Optimization for PM 2.5 Forecasting in IoT Networks
  Abstract This study presents a lightweight and privacy-preserving federated learning framework designed for resource-constrained IoT sensor networks, emphasizing efficient distributed computation across heterogeneous devices. The framework enables decentralize…
  preprints Hadi Nazari¹, Yaghoub Farjami², Ali TaeiZadeh³ 2025 置信度 0.74
• Client-Centered Federated Learning for Heterogeneous EHRs: Use Fewer Participants to Achieve the Same Performance
  europepmc 2025 置信度 0.80
• Comparative Analysis of Federated Learning (FL), Reinforcement Learning (RL) and Evolution Strategy (ES) in Gaming Environments
  europepmc 2025 置信度 0.80
• Federated Learning with Model Personalization: A Survey
  Abstract Federated Learning (FL) enables multiple clients to train models collaboratively without sharing local data, but a single global model often struggles with data heterogeneity. Personalized Federated Learning (PFL) addresses this by tailoring models to…
  preprints Ethan Weimann 2025 置信度 0.74