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• Peer Review #3 of "OES-Fed: a federated learning framework in vehicular network based on noise data filtering (v0.1)"
  crossref 2022-09-25T02:33:00Z 置信度 0.70
• Review for "Federated Deep Learning for Triple Bottom Line Optimization in Virtual Refrigeration Through Simulation-Based Sustainable Food Management"
  crossref 2026-01-22T21:09:02Z 置信度 0.70
• Digital Twin-Assisted Personalized Federated Reinforcement Learning for Cyber Defense in Internet of Medical Things
  Abstract This article proposes a Digital Twin-Assisted Robust Personalized Federated Reinforcement Learning framework, termed DSGTPFL, for adaptive cyber defense in IoMT networks. The proposed approach integrates six complementary mechanisms: (i) graph neural …
  preprints maryam fattahi 2026 置信度 0.74
• Image-based crop disease detection with federated learning
  Abstract Crop disease detection and management is critical to improving productivity, reducing costs, and promoting environmentally friendly crop treatment methods. Modern technologies, such as data mining and machine learning algorithms, have been used to dev…
  crossref Denis MAMBA KABALA, Adel HAFIANE, Laurent BOBELIN, Raphael CANALS 2023-07-14T03:35:10Z 置信度 0.70
• Trustworthy AI-Powered Intrusion Detection for the Internet of Medical Things (IoMT): A Review.
  The Internet of Medical Things (IoMT) is transforming healthcare through continuous patient monitoring, telemedicine, cloud-edge services, and Healthcare 5.0. However, the rapid growth of interconnected medical devices has expanded the healthcare cyberattack s…
  pubmed Islam J, Datta D, Akhter F 2026 置信度 0.82
• Quantum-Augmented Federated AI for Adaptive Pharmacogenomic Precision Oncology: A Perspective.
  Precision oncology has evolved from single-gene biomarker testing toward multimodal molecular and clinical profiling; however, most therapeutic decisions remain based on static baseline assessments that inadequately capture tumor evolution, treatment response,…
  pubmed Rafiq Z, Puebla Osorio N 2026 置信度 0.82
• Cross-Silo Federated Learning for Predicting Successful Mechanical Ventilation Weaning Across 5 Intensive Care Unit Databases: Retrospective Database Analysis.
  The prediction of weaning from mechanical ventilation (MV) can support clinical decision-making and help reduce the risk of weaning failure in intensive care units (ICUs). Cross-silo federated learning (FL) offers a promising approach to developing robust pred…
  pubmed Sheikhalishahi S, Kaspar M, Schwinn J, Morhart M 等 2026 置信度 0.82
• Federated Deep Learning and TinyML Co-Design for Securing Resource-Constrained IoT and SCADA Networks: A PRISMA-Compliant Systematic Review, Taxonomy and Statistical Meta-Analysis
  europepmc 2026 置信度 0.80
• Efficient collaborative learning of the average treatment effect.
  In response to the growing need for generating real-world evidence from multisite collaborative studies, we introduce an efficient collaborative learning approach to evaluate average treatment effect (ECO-ATE) in a multisite setting under data-sharing constrai…
  pubmed Li S, Duan R 2026 Apr 9 置信度 0.82
• Artificial intelligence in cardio-oncology: decoding mechanisms, predicting toxicity, and personalizing cancer therapy.
  Cancer therapy-related cardiovascular toxicity (CTR-CVT) threatens the sustainability of oncological advancements, demanding innovative approaches for early risk stratification. This review synthesizes how artificial intelligence (AI) is redefining cardio-onco…
  pubmed Yu C, Jiang L, Long L, Yu H 2026 置信度 0.82
• Multimodal artificial intelligence in retinopathy of prematurity: A comprehensive narrative review.
  Retinopathy of prematurity (ROP) remains a leading cause of preventable childhood blindness globally, particularly in regions with limited screening resources. Traditional diagnosis relying on subjective interpretation of fundus images faces challenges of inte…
  pubmed Zhao X, Wu Z, Wu S, Cui K 等 2026 May 14 置信度 0.82
• Artificial intelligence in diabetes care: Toward precision diagnosis and personalized management.
  Diabetes mellitus is a global health challenge requiring innovative solutions for early diagnosis, personalized treatment, and ongoing management. This review aims to examine the impact of artificial intelligence (AI) on diabetes care, focusing on precision di…
  pubmed Aburjai T, Ali Agha ASA, Abuhilaleh Y, Miqdad A 等 2026 置信度 0.82
• Liquid Biopsy in Colorectal Cancer: Future Perspectives Through the Lens of Artificial Intelligence-A Comprehensive Review of Novel Literature.
  Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, with prognosis critically dependent on the stage at diagnosis. Traditional tissue biopsy presents well-known limitations, including tumor heterogeneity and invasiv…
  pubmed Paduraru DN, Palcău AC, Gorecki GP, Dinulescu A 等 2026 Apr 29 置信度 0.82
• Bridging Traditional Modeling and Artificial Intelligence in Measles Epidemiology: Methods, Applications, and Future Directions-A Narrative Review.
  Measles remains one of the most contagious infectious diseases globally and continues to pose substantial public health risks despite decades of effective vaccination. This narrative review examines both classical and contemporary computational approaches used…
  pubmed Baiasu AF, Rotaru-Zavaleanu AD, Boldea AM, Ruscu MA 等 2026 Apr 24 置信度 0.82
• Communication-Efficient Federated Learning with Dual-Sided Sparse Aggregation for Edge Sensing Systems.
  Distributed edge sensing systems, such as IoT monitoring nodes, wearable devices, and camera-based sensing terminals, continuously generate privacy-sensitive data that are costly to transmit to a central server. Federated learning (FL) provides a promising sol…
  pubmed Zhao H, Li J 2026 置信度 0.82
• A Federated Approach for Adaptive Urban Sound Classification on TinyML Edge Devices.
  Cities exhibit sound patterns that vary across locations and time, while transmitting raw audio introduces communication and privacy concerns. We present a federated TinyML architecture for real-time urban sound classification on microcontroller-class edge dev…
  pubmed Trigkas A, Piromalis D, Papageorgas P 2026 置信度 0.82
• Governing Privacy-Preserving Face Recognition in Transport Infrastructures: A Comprehensive Review.
  Face recognition technologies are increasingly deployed in transport infrastructures to improve efficiency and security, but they raise significant privacy and data protection concerns. This study reviews how privacy-preserving face recognition techniques can …
  pubmed Sanz EMB, Gonzalo Primo A, Choudhary G, Dragoni N 2026 置信度 0.82
• Spiking Neural Networks with Continual Learning for Steering Angle Regression: A Sustainable AI Perspective.
  This work explores the application of Spiking Neural Networks (SNNs) and Continual Learning (CL) methodologies to the problem of steering angle regression, using autonomous driving simulation as the experimental context, with a focus on energy efficiency and a…
  pubmed Martínez FS, Costa S, Parada R 2026 Apr 24 置信度 0.82
• Resource-Adaptive Semantic Transmission and Client Scheduling for OFDM-Based V2X Communications.
  Proportional, fair scheduling in OFDM-based vehicle-to-everything (V2X) uplink causes the resource-block allocation of each vehicle to vary from slot to slot, yet conventional semantic encoders produce a fixed number of output tokens regardless of the instanta…
  pubmed Liu J, Chen Y, Wu W, Tian F 2026 Apr 23 置信度 0.82
• Dual asymmetric momentum improves federated class unlearning in edge systems.
  Federated learning is increasingly used in edge and on-device systems, where models may later need to reduce the influence of specific training data in response to governance or deletion requests. Doing this after training is difficult because full retraining …
  pubmed Patra A, Mayaluri ZL, Sahoo PK, Kumawat G 2026 May 12 置信度 0.82
• Preliminary Investigation of Federated Learning for MACE Prediction from Electronic Medical Records: A Multicontinental Study.
  Machine learning models for predicting major adverse cardiovascular events (MACE) often generalize poorly across populations, and multinational development is limited by data-sharing constraints.
  pubmed Slamanig G, Kalabakov S, Lorenzer L, Schrempf M 等 2026 May 7 置信度 0.82
• Decoupling forgetting and preservation in federated unlearning via knowledge distillation.
  Federated Learning (FL) enables collaborative model training without sharing raw data, but compliance with data privacy regulations such as the "Right to be Forgotten" requires mechanisms to remove specific clients' contributions from trained models. A key cha…
  pubmed Kwon H, Maeng JB, Kim DJ 2026 May 11 置信度 0.82
• Federated Function-on-function Regression with an Efficient Gradient Boosting Algorithm for Privacy-Preserving Telemedicine.
  Federated Learning (FL) is an emerging computing paradigm to collaboratively train Machine Learning (ML) models across multi-source data while preserving privacy. The major challenge of "meaningful" implementation of FL for any ML model is how to guarantee tha…
  pubmed Ding Y, Costa C, Si B 2026 置信度 0.82
• Artificial intelligence in Kellgren-Lawrence grading of knee osteoarthritis: bridging radiographic tradition with algorithmic precision.
  Knee osteoarthritis (KOA) remains the most prevalent form of osteoarthritis and a major cause of global disability. The Kellgren-Lawrence (KL) grading system, though widely used, suffers from inter- and intra-observer variability, especially in early disease s…
  pubmed Rawat S, Chaturvedi VP, Vaidya B, Shanmugam H 等 2026 置信度 0.82
• Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable.
  This work tackles the fundamental challenges in Federated Learning (FL) posed by arbitrary client participation and data heterogeneity, prevalent characteristics in practical FL settings. It is well-established that popular FedAvg-style algorithms struggle wit…
  pubmed Ying B, Li Z, Yang H 2025 Dec 置信度 0.82
• Big Data and Trustworthy AI for Heart Failure: A Review.
  The rapid evolution of machine learning techniques, combined with the growing availability of large and diverse datasets, is poised to transform heart failure research and clinical care. This review first provides an overview of key machine learning and artifi…
  pubmed Perramon-Llussà J, Skorupko G, Rao S, Ruiz Pujadas E 等 2026 置信度 0.82
• Federated multimodal learning for privacy-preserving blood donor profiling and personalized recall strategy optimization.
  Blood supply shortages remain a persistent challenge for transfusion services, yet effective donor recall is hampered by fragmented data across institutions, limited profiling depth, and increasing privacy regulations. This paper proposes a federated multimoda…
  pubmed Li L, Li J 2026 May 9 置信度 0.82
• Federated sparse-learning framework for detecting low-rate stealth attacks in resource-constrained IoT devices.
  Low-rate stealth attacks present a major challenge in Internet of Things (IoT) environments because their slow, irregular, and noise-like traffic patterns evade traditional rate-based intrusion detection systems. To address this problem, this paper proposes FS…
  pubmed Anandhi R, Srikanth GU 2026 May 9 置信度 0.82
• CSF-net: a color space fusion network with self-attention-driven feature learning for feline ocular diseases classification.
  Feline ocular diseases can cause irreversible vision loss if they are not detected early. However, early diagnosis is often difficult. This is due to limited access to veterinary ophthalmology services and the challenge of distinguishing between visually simil…
  pubmed S P D, Lee M, Yang HJ 2026 置信度 0.82
• From Pixels to Precision: Generative Artificial Intelligence as a Paradigm Shift in Spine Imaging-Technical Foundations, Clinical Applications, and the Path to Safe Clinical Deployment.
  Spine imaging represents a complex diagnostic frontier characterized by anatomical variability, motion artifacts, metallic instrumentation interference, and significant inter-reader diagnostic variability (κ=0.20 across institutions). While conventional …
  pubmed Ashraf D, Sanker V, Liverani L, Park C 等 2026 Apr 置信度 0.82
• Adaptive multimodal learning for driver cognitive state monitoring using transformer-based fusion with personalized meta-learning and federated optimization.
  Road accidents caused by driver fatigue and cognitive overload remain a significant public safety concern. According to recent traffic safety data, drowsy driving contributes to thousands of fatal accidents each year, emphasizing the urgent need for intelligen…
  pubmed Abinaya G, Dinakaran K 2026 May 6 置信度 0.82
• DeCAF: Decentralized consensus-and-factorization for low-rank adaptation of foundation models.
  Low-Rank Adaptation (LoRA) has emerged as one of the most effective, computationally tractable fine-tuning approaches for training Vision-Language Models (VLMs) and Large Language Models (LLMs). LoRA accomplishes this by freezing the pre-trained model weights …
  pubmed Saadati N, Jiang Z, Waite JR, Ganguly S 等 2026 Oct 置信度 0.82
• FedTFT: Federated Temporal Fusion Transformer for Interpretable Multi-Horizon Psychiatric Risk Prediction Across Cross-Silo Hospitals.
  Psychiatric inpatient monitoring generates multimodal data, but privacy constraints and cross-hospital heterogeneity limit centralized learning. We propose FedTFT, a federated Temporal Fusion Transformer for multi-horizon psychiatric risk prediction with horiz…
  pubmed Ahamed A, Kang RR, Lee K 2026 May 5 置信度 0.82
• Adaptive homomorphic federated learning framework for multi-institutional medical imaging with optimized diagnostic accuracy.
  The growing use of artificial intelligence (AI) in medicine has highlighted the imperative for privacy-preserving and high-accuracy diagnostic systems. Traditional federated learning (FL) solutions allow multi-institutional collaborative training of a model wi…
  pubmed Josephine Usha L, Saranya KR, Suganya Y, Mallikka R 2026 置信度 0.82
• Predicting blood transfusion after ICU admission in five databases: A comparison of three machine learning paradigms.
  The objective of this retrospective study is to compare three learning approaches for blood transfusion (BT) prediction after intensive care unit (ICU) admission: local learning (LL), federated learning (FL), and centralized learning (CL) across five ICU datab…
  pubmed Schwinn J, Sheikhalishahi S, Morhart M, Soto Rey I 等 2026 Jan-Dec 置信度 0.82
• Integrating AI-enhanced kinase enrichment analysis (KEA) with geometric deep learning and federated learning for precision drug repurposing.
  Artificial intelligence (AI) is reshaping drug repurposing by integrating systems biology with molecular design. Here, we present a unified framework combining AI-enhanced Kinase Enrichment Analysis (KEA), geometric deep learning, and federated learning to ena…
  pubmed Iqbal S, Chen J, Pal D, Shen B 2026 置信度 0.82
• Adaptive aggregation in federated learning for retinal vein occlusion detection: A dynamic approach based on data distribution.
  Clinical decision support systems (CDSS) increasingly rely on artificial intelligence (AI) to interpret biomedical images and provide accurate, real-time diagnostics. Federated Learning (FL) has emerged as a promising privacy-preserving solution for training A…
  pubmed Sri AV, Pusapati B, Puli RS, Morampudi MK 2026 Jun 置信度 0.82
• Gradient Normalization Enables Communication-Efficient Distributed Learning Under Initialization Data Heterogeneity.
  Communication-efficient distributed learning has achieved remarkable progress in training large-scale deep neural networks across numerous clients. However, in heterogeneous environments-where local data distributions vary significantly-the empirical performan…
  pubmed Sun T, Wu B, Liu X, Yuan K 2026 Sep 置信度 0.82
• Multimodal Intelligent Monitoring of Parkinson Disease: Scoping Review of Progress and Translational Challenges.
  Parkinson disease (PD) is a progressive neurodegenerative disorder with a rapidly growing global prevalence. Current clinical assessments, such as the Unified Parkinson Disease Rating Scale, are limited by subjectivity and episodic application, creating a need…
  pubmed Tan J, Deng X, Wu C, Yao M 等 2026 Apr 17 置信度 0.82
• Utility-Preserving Federated Graph Learning With Dual-Perspective Fairness.
  Fairness-aware federated graph neural networks (FedGNNs) necessitate consideration of both the server and the clients. However, fairness-aware methods struggle to enhance dual-perspective (i.e., server and clients) fairness without sacrificing utility due to t…
  pubmed Luo R, Huang H, Yu S, Yu F 等 2026 置信度 0.82
• [Current applications and prospects of artificial intelligence in perioperative comprehensive management for gastrointestinal surgery].
  The shift from the traditional empirical approach to a more data-driven method in the diagnosis and treatment of GI cancers is significant due to advancements in overall precision medicine and digital healthcare. Artificial intelligence(AI),the driving force b…
  pubmed Wang PY, Liu B, Liu X, Shan MH 等 2026 Jun 1 置信度 0.82
• Advancing federated semi-supervised medical image segmentation: A duo of interactive denoising pseudo-labels and convolutional contrastive learning.
  Many existing studies on federated learning (FL) for segmentation primarily assume that all client data are labeled. However, in reality, due to the high cost of hospital construction and the scarcity of expert annotators, many medical sites can only provide u…
  pubmed Xu Z, Zhang Y, Li B, Zhou G 等 2026 Jul 置信度 0.82
• Adaptive trust evaluation and representation-based robust aggregation against poisoning attacks in federated learning.
  Federated Learning (FL) has emerged as a prominent paradigm for privacy-preserving distributed training. However, particularly in non-IID and open-participation environments, FL remains highly vulnerable to model poisoning, specifically backdoor attacks. Exist…
  pubmed Xiao Y 2026 Apr 28 置信度 0.82
• Design of a dynamics-based hydraulic controller for lifting manipulator wrist and its stability analysis.
  Under the requirements of Industry 4.0, the performance requirements for improving the hydraulic controller of the mechanical hand wrist are getting higher and higher. To address issues such as response delay and insufficient accuracy in traditional methods, t…
  pubmed Li B 2026 置信度 0.82
• Diabetic Retinopathy Detection: AI Models and Approaches.
  Diabetic retinopathy (DR), a major cause of vision loss worldwide, results from chronic diabetes damage to retinal blood vessels. Vision loss can be prevented if DR is detected early, but traditional retinal screening by eye care takes time and expertise. Rece…
  pubmed Madi MMM, Farr PC, Bester D 2026 置信度 0.82
• BF2GAN: A Fused Federated Learning-Based Biometric Enabled Deep Learning Approach for Secure Medical Image Sharing in Cloud Environment.
  Medical images contain sensitive and private health information of patients, which is crucial to be safeguarded from unauthorized access. Various encryption schemes are used for improving security; however, they are subject to adversaries associated with highe…
  pubmed Mohammed ZA, Rajeshwari D, Mohammed S, Sreelaxmi M 2026 Apr 27 置信度 0.82
• Designing a children's health exposomics study protocol: The CHILDREN_FIRST multi-country prospective cohort using multi-omics and personalized prevention approaches.
  Non-communicable diseases (NCDs) account for ~71% of all deaths globally, including 15 million premature deaths each year (deaths between 30-69 years of age). Instead of waiting until disease manifestation, focusing on the origins of NCDs during childhood offe…
  pubmed Konstantinou C, Soursou G, Abimbola S, Charisiadis P 等 2026 置信度 0.82
• Six artificial intelligence innovation strategies applied to autism spectrum disorder research: A narrative review.
  Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by deficits in social communication and restricted behaviors. Traditional assessment and intervention methods rely heavily on subjective and time-consuming approaches, which…
  pubmed Zhang T, Cao Z, Li W, Lv Z 2026 置信度 0.82
• MRI-based brain stroke classification using a hybrid vision transformer-BiLSTM architecture.
  Stroke is a prominent cause of long-term disability, impacting patients' socioeconomic status in daily life. Hemorrhagic and ischemic strokes differ in dimensions, forms, and locations, posing challenges for automated detection. Magnetic resonance imaging (MRI…
  pubmed Samuel R, Pandi T 2026 置信度 0.82
• Neuroimaging and machine learning fusion for improved brain tumor diagnosis and prognosis.
  Brain diseases, including tumors, along with mental and neurological disorders, pose a substantial threat to millions of individuals worldwide. Doctors use both structural and functional neuroimaging methods for diagnosing these diseases. Specialist doctors of…
  pubmed Khan UA, Badri S, Hasan SH 2026 Apr 26 置信度 0.82
• From bench to byte: A UK perspective on data-driven cancer research.
  Cancer research is undergoing a profound transformation driven by the rapid expansion of clinical, genomic, imaging, and real-world data. As Europe prepares for the implementation of the European Health Data Space (EHDS), the ability of health systems to effec…
  pubmed Wooller SK, Blake A, McCabe M, McLean C 等 2026 Jun 3 置信度 0.82
• The emerging use of artificial intelligence in safety pharmacology and toxicology.
  Safety pharmacology is concerned with the identification and characterization of adverse effects of drug candidates on vital organ systems. The emergence of artificial intelligence (AI) and machine learning (ML) has prompted growing interest in their potential…
  pubmed Miller L, Treleaven I, El Amrani AI, Rossman EI 等 2026 Jun 置信度 0.82
• FedSemiDG: Domain generalized federated semi-supervised medical image segmentation.
  Medical image segmentation is challenging due to the diversity of medical images and the lack of labeled data, which motivates recent developments in federated semi-supervised learning (FSSL) to leverage a large amount of unlabeled data from multiple centers f…
  pubmed Deng Z, Xu Z, Isshiki T, Zheng Y 2026 Jul 置信度 0.82
• Graph Neural Networks in Neuroimaging: Current Status and Biostatistical Considerations for Clinical Deployment.
  Graph Neural Networks (GNNs) have emerged as a novel paradigm that enables scientists to model complex relational data in medical applications, offering unique advantages over traditional deep learning (DL) approaches for non-Euclidean domains. This paper prov…
  pubmed Kumar R, Sporn K, Waisberg E, Ong J 等 2026 Aug 置信度 0.82
• Agentic AI as a coordination paradigm in digital health and agri-food systems.
  Digital health and agri-food data systems increasingly rely on sophisticated machine learning and data-sharing infrastructures. Yet persistent challenges in scalability, accountability, and public trust indicate that technical capability alone does not resolve…
  pubmed Gavai AK, Meuwissen MPM 2026 Mar 13 置信度 0.82
• Architectural and translational perspectives on clinical decision support systems for rare disease diagnosis: a scoping review.
  Rare diseases remain difficult to diagnose because of phenotypic heterogeneity, limited clinical familiarity, and fragmented health data infrastructures. Clinical decision support systems (CDSS) have emerged as promising tools to support earlier recognition an…
  pubmed Özçetin E, Baş SE, Özpay F 2026 Jul 15 置信度 0.82
• Adapting federated radiomics models for radiation pneumonitis prediction in patients receiving thoracic radiotherapy with immunotherapy.
  Radiation pneumonitis (RP) is one of the major dose-limiting toxicities of thoracic radiotherapy. Although multiple studies have attempted to predict RP, robust multicenter model development is often hindered by privacy regulations and data-transfer constraint…
  pubmed Zhu Z, Yan M, Ji W, Zhang Z 等 2026 置信度 0.82
• Federated Inverse Probability Treatment Weighting for Individual Treatment Effect Estimation.
  Individual Treatment Effect (ITE) estimation is to evaluate the causal effects of treatment strategies on some important outcomes, which is a crucial problem in healthcare. Most existing ITE estimation methods are designed for centralized settings. However, in…
  pubmed Yin C, Chen HY, Chao WL, Zhang P 2026 Apr 置信度 0.82
• FedSKD: Aggregation-Free Model-Heterogeneous Federated Learning via Multidimensional Similarity Knowledge Distillation for Medical Image Classification.
  Federated learning (FL) enables privacy-preserving collaborative model training without direct data sharing. Model-heterogeneous FL (MHFL) enables clients to train personalized models with heterogeneous architectures, but existing methods mainly rely on centra…
  pubmed Weng Z, Cai W, Zhou B 2026 Apr 22 置信度 0.82
• Forgettable Federated Linear Learning With Certified Data Unlearning.
  The advent of federated learning (FL) has revolutionized the way distributed systems handle collaborative model training while preserving user privacy. Recently, federated unlearning (FU) has emerged to address demands for the "right to be forgotten" and unlea…
  pubmed Jin R, Chen M, Zhang Q, Li X 2026 Apr 22 置信度 0.82
• Artificial intelligence and wearable sensors in sports injury risk prediction: current status and future perspectives.
  Artificial intelligence (AI) and wearable sensors are increasingly reshaping sports injury risk prediction by enabling continuous, individualized, and data-driven assessment.
  pubmed Dong G, Tan X, Yuan P, Li Y 2026 Dec 置信度 0.82
• AI-Driven Chemometrics for Multi-omics Data Integration: Advances, Challenges, and Future Directions.
  The convergence of artificial intelligence and chemometrics has revolutionized multi-omics data integration, enabling unprecedented insights into complex biological systems. This critical review examines AI-driven approaches for integrating genomics, proteomic…
  pubmed Polu PR 2026 Apr 20 置信度 0.82
• Zero knowledge verifiable, semi asynchronous federated learning for trajectory prediction on permissioned blockchain.
  Vehicle trajectory prediction in Internet-of-Vehicles requires collaborative learning over sensitive trajectories under intermittent connectivity and partially trusted participants. ChainDrive-FL-VRA coordinates semi-asynchronous federated learning on a permis…
  pubmed Raveendra Reddy K, Muralidhar A 2026 Apr 20 置信度 0.82
• Building trust and privacy in cross-border health data sharing for European cancer research.
  Data-driven research using artificial intelligence (AI) is transforming biomedical science, yet its application in medical imaging remains limited by fragmented datasets, heterogeneous legislation, and ethical uncertainties. The European Cancer Imaging Initiat…
  pubmed Martínez RM, de Marco A, Blanquer I, Martí-Bonmatí L 2026 Mar 置信度 0.82
• Three-Dimensional human motion analysis using LiDAR technology: A systematic review.
  3D Light Detection and Ranging (LiDAR) has gained increasing attention in the field of human motion analysis due to its capability to non-invasively capture dynamic 3D pose data. However, accurate extraction of motion information from sparse and unordered poin…
  pubmed Lai J, Yavari M, Lee PVS, Ackland DC 2026 Jun 置信度 0.82
• Machine learning paradigms in natural and engineered water systems: From proof-of-concept to trustworthy deployment.
  Machine learning is increasingly used to model and manage water systems, from rivers and aquifers to treatment plants and distribution networks. Yet many studies remain proof-of-concept: models are trained on sparse or siloed data, behave as black boxes and ra…
  pubmed Ma R, Li J, Zhang Z, Liu Y 等 2026 Aug 1 置信度 0.82
• A federated learning framework for deep imputation of missing data in heterogeneous ICU time series.
  The proliferation of multimodal time-series healthcare data presents unprecedented opportunities for data-driven insights but also significant challenges due to pervasive missing values, especially in critical care environments. Traditional centralised imputat…
  pubmed Vavekanand R, Sathio AA, Sultani M, Raja Vavekanand 等 2026 置信度 0.82 Computer scienceMissing dataImputation (statistics)Data miningTime series
• A review on AI-enabled drug design in medicinal chemistry: Analytical validation, challenges, and regulatory considerations.
  Artificial intelligence, particularly machine learning, has profoundly reshaped drug discovery, addressing longstanding challenges such as exorbitant costs and protracted timelines. Conventional approaches often exceed $2.6 billion per drug over 12-15 years, w…
  pubmed Kumar S, Misra SK, Tiwari A, Katiyar A 等 2026 Oct 1 置信度 0.82
• The application of artificial intelligence in the intersection of metabolic dysfunction-associated steatotic liver disease and cardiovascular diseases.
  There is high comorbidity and complex pathological mechanisms between metabolic dysfunction-associated steatotic liver disease (MASLD) and cardiovascular disease (CVD), and the accuracy of traditional risk assessment tools is insufficient. The paper highlights…
  pubmed Kong Y, Chen H, Chen Y, Wang C 2026 置信度 0.82
• Secure yet fragile: adversarial vulnerabilities of federated vision-language models in medical AI.
  Vision-Language Models (VLMs) enable powerful multimodal reasoning for medical image analysis, while federated learning allows collaborative training across institutions without sharing patient data. However, the adversarial robustness of federated medical VLM…
  pubmed Fime AA, Samiha TZ, Hossain MZ, Zaman S 等 2026 Apr 16 置信度 0.82
• The Intersection of AI and genomics in health and disease: Advancements and applications.
  AI and genomics are revolutionizing precision medicine by using machine learning (ML) to analyze large-scale next-generation sequencing (NGS) data, identifying genetic mutations and biomarkers for personalized therapies. In practice, this accelerates drug disc…
  pubmed Kaushik L, Vivek AT, Arora S, Hamid F 等 2026 置信度 0.82
• Multi-site distributed training with data protections for PET-based synthetic CT.
  BACKGROUND: Accurate PET quantification relies on attenuation correction (AC), commonly performed using a linear attenuation map derived from a CT acquisition. However, CT can introduce misregistration artifacts and adds radiation dose. Synthetic CT (sCT) from…
  pubmed Jørgensen K, Partin L, Ashok R, Shah V 等 2026 Apr 15 置信度 0.82
• Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.
  The intricate interplay between the brain and heart underpins both physiological regulation and pathophysiological processes, yet decoding these interactions remains a formidable challenge. Recent advances in artificial intelligence (AI) offer transformative o…
  pubmed Varzideh F, Pande S, Jankauskas SS, Mone P 等 2026 置信度 0.82
• Empowering photodynamic therapy with artificial intelligence: current trends and future directions.
  The evolution of photodynamic therapy (PDT), from ancient photomedicine practices to modern clinical applications, reflects its remarkable versatility in oncology and beyond. PDT relies on the interaction between photosensitizers, light, and tissue oxygen to g…
  pubmed Paul A, Xavierselvan M, Aebisher D, Kubrak T 等 2026 置信度 0.82
• Federated Gastrointestinal Lesion Classification with Clinical-Entropy Guided Quantum-Inspired Token Pruning in Vision Transformers.
  Background: Gastrointestinal (GI) cancers remain a major global health concern, where timely and accurate interpretation of endoscopic findings plays a decisive role in patient outcomes. In recent years, deep learning-based decision support systems have shown …
  pubmed Awais M, Qamar AM, Khalid U, Khan RU 2026 置信度 0.82
• Artificial intelligence in microbiology: implications for metagenomics, diagnostics, and AMR surveillance.
  Artificial intelligence (AI) is now a key player in modern microbiology, as it enables high-resolution analyses of genomic, metagenomic, and clinical data for the monitoring of infectious disease and antimicrobial resistance (AMR). Considerable advancements in…
  pubmed Khangarot R, Kumari V, Mishra R, Singh A 2026 置信度 0.82
• An efficient hybrid federated learning framework for pneumonia diagnosis with proximal optimization and parameter-efficient adaptation.
  Pneumonia remains a serious worldwide health concern, particularly in low-resource countries, where prompt diagnosis is challenging. Early detection relies on chest radiography; however, data privacy rules and patient data fragmentation make it difficult to bu…
  pubmed Gupta C, Gupta R, Gill NS, Gulia P 等 2026 Apr 11 置信度 0.82
• HEAL-AI: Enabling proactive and personalized smart healthcare through hierarchical edge autonomous learning.
  This study aims to develop a proactive, personalized, and privacy-preserving smart healthcare framework that enables real-time clinical decision support across distributed healthcare environments while ensuring interpretability, low latency, and regulatory com…
  pubmed Othman MA 2026 置信度 0.82
• Performance of federated versus centralized learning for mammography classification across film-digital domain shift.
  Large, diverse datasets are essential for reliable deep learning in mammography, yet clinical data remain siloed due to privacy and governance constraints. Federated learning enables collaborative training without sharing raw data, but its robustness under str…
  pubmed Ali Y, Müller J, Weinmann A, Gregori J 2026 置信度 0.82
• Integrating AI-blockchain framework with Spider Monkey Federated Extreme Learning for enhanced healthcare data protection.
  The Internet of Medical Things (IoMT) has largely revolutionized the healthcare sector because of its rapid growth that allows continuous monitoring and data-driven services that are based on intelligence. Nonetheless, such a rising connectivity also heightens…
  pubmed Nishok VS, Dhanasekaran S, Suresh G, Anandaraj APS 2026 置信度 0.82
• Supply chain information security sharing technology based on blockchain consensus algorithm and federated learning.
  Existing supply chain information security methods all suffer from the difficulty of balancing information sharing efficiency and information privacy protection. Data is prone to leakage, resulting in an increase in the overall risk of the supply chain. In res…
  pubmed Xu D, Li J, Ren Z 2026 置信度 0.82
• Can Artificial Intelligence Be Used to Predict Response in Rectal Cancer? Current Evidence and Future Possibilities.
  Artificial intelligence (AI) offers a promising solution to the long-standing challenge of accurately predicting treatment response in rectal cancer. In this narrative review, we summarize current AI-driven approaches to predicting pathologic complete response…
  pubmed Lopez NE, Neel NC 2026 May 置信度 0.82
• Federated Choquet Regression with LASSO for Outcome Prediction in Multisite Longitudinal Trial Data.
  Aggregating person-level data across multiple clinical study sites is often constrained by privacy regulations, necessitating the development of decentralized modeling approaches in biomedical research. To address this requirement, a federated nonlinear regres…
  pubmed Lomasov S, Fang H, Wang H 2026 Jan 置信度 0.82
• Federated multi-label text feature selection via manifold-aware sparse modeling and cooperative grey wolf optimization.
  Feature selection (FS) for multi-label text classification faces issues such as high dimensionality, strong label correlations, and sparse features, which often lead to suboptimal feature subsets. Moreover, most existing methods are centralized and thus ill-su…
  pubmed Zheng Y, Ye Z, Zhang S, Wang K 2026 置信度 0.82
• EMM-Det: Energy-Efficient Multidrone Tiny Object Detection by Memory-Enhanced Spiking Neural Networks.
  Unmanned aerial vehicles (UAVs) play a vital role in scenarios such as community safety patrol and disaster search-and-rescue operations due to their maneuverability and deployment flexibility. However, their limited payload capacity, energy constraints, and s…
  pubmed Cai Z, Luo H, Liu T, Xia Y 等 2026 Apr 7 置信度 0.82
• Federated Meta-Analysis of HEART Score Performance for Emergency Department Chest Pain.
  Multicenter evaluation of emergency department (ED) risk stratification tools is often limited by barriers to patient-level data sharing. We used the HEART score as a clinical use case to evaluate whether a federated diagnostic meta-analytic approach yields pe…
  pubmed Wang H, Chou E, Robinson RD, Farzad A 等 2026 Apr 置信度 0.82
• Scaling Smart Cities with Federated Learning: Balancing Accuracy and Privacy for Building Energy Performance Prediction
  The building energy sector is a significant contributor to carbon emissions, thereby playing a crucial role in driving global sustainability efforts to achieve the net-zero targets outlined in the Paris Climate Agreement. Precise predictions of building energy…
  datacite Delgado Fernandez, Joaquin, Willburger, Lukas, Wiethe, Christian, Wenninger, Simon 等 2026 置信度 0.66 Building energy performanceEnergy quantification methodsFederated learningPrivacySmart city
• extendedseek/BoneFormer: BoneFormer v0.1.0
  Initial public release of BoneFormer-KG. This repository provides the implementation of BoneFormer-KG for federated knowledge-guided 3D Transformer learning for bone tumor segmentation from partially annotated CT data. This release includes the source code, co…
  datacite extendedseek 2026 置信度 0.66
• extendedseek/BoneFormer: BoneFormer v0.1.0
  Initial public release of BoneFormer-KG. This repository provides the implementation of BoneFormer-KG for federated knowledge-guided 3D Transformer learning for bone tumor segmentation from partially annotated CT data. This release includes the source code, co…
  datacite extendedseek 2026 置信度 0.66
• Reproducibility package for Threat-Intelligence-Aware Federated Intrusion Detection for Industrial IoT
  Derived result tables, figure source data, scripts, configuration snapshots and non-sensitive logs supporting the manuscript tables and figures. Raw third-party datasets are not redistributed. License note: MIT for code/scripts; CC BY 4.0 for project-created n…
  datacite Xie, Fucai 2026 置信度 0.66 federated learningintrusion detectionIndustrial IoTcyber threat intelligencepoisoning resilience
• Reproducibility package for Threat-Intelligence-Aware Federated Intrusion Detection for Industrial IoT
  Derived result tables, figure source data, scripts, configuration snapshots and non-sensitive logs supporting the manuscript tables and figures. Raw third-party datasets are not redistributed. License note: MIT for code/scripts; CC BY 4.0 for project-created n…
  datacite Xie, Fucai 2026 置信度 0.66 federated learningintrusion detectionIndustrial IoTcyber threat intelligencepoisoning resilience
• A Robust Self-Supervised Contrastive Learning Framework for Secure and Reliable Financial Transaction Mining
  ABSTRACT The rapid expansion of digital payment ecosystems, online banking platforms, and fintech services has led to an unprecedented growth in financial transaction data. While this digital transformation enhances accessibility and efficiency, it also increa…
  datacite Mrs. J Nagapriya, Dr. M. Princerani, Dr. Mohamed Kaisarul Haq, Mohammad Shah Alam Chowdhury 2026 置信度 0.66 Keywords: Self-Supervised Learning, Contrastive Learning, Financial Transaction Mining, Fraud Detection, Anomaly Detection, Artificial Intelligence, Secure Analytics.
• A Robust Self-Supervised Contrastive Learning Framework for Secure and Reliable Financial Transaction Mining
  ABSTRACT The rapid expansion of digital payment ecosystems, online banking platforms, and fintech services has led to an unprecedented growth in financial transaction data. While this digital transformation enhances accessibility and efficiency, it also increa…
  datacite Mrs. J Nagapriya, Dr. M. Princerani, Dr. Mohamed Kaisarul Haq, Mohammad Shah Alam Chowdhury 2026 置信度 0.66 Keywords: Self-Supervised Learning, Contrastive Learning, Financial Transaction Mining, Fraud Detection, Anomaly Detection, Artificial Intelligence, Secure Analytics.
• The Connect.CI Portal: Building and Sustaining a Cyberinfrastructure Community Platform
  ConnectCI is a web-based platform that facilitates collaboration across the national research computing ecosystem. Since its introduction at PEARC21, the platform has evolved from a project management tool for regional cyberteams to a comprehensive community i…
  datacite Ma, Julie, Fein, Lissie, Pasquale, Andrew, Gazula, Vikram 等 2026 置信度 0.66 cyberinfrastructureresearch computingDrupalopen sourcecommunity portal
• The Connect.CI Portal: Building and Sustaining a Cyberinfrastructure Community Platform
  ConnectCI is a web-based platform that facilitates collaboration across the national research computing ecosystem. Since its introduction at PEARC21, the platform has evolved from a project management tool for regional cyberteams to a comprehensive community i…
  datacite Ma, Julie, Fein, Lissie, Pasquale, Andrew, Gazula, Vikram 等 2026 置信度 0.66 cyberinfrastructureresearch computingDrupalopen sourcecommunity portal
• A scalable and privacy-aware framework for connected vehicle systems using smartphone-based multimodal sensing
  Connected Vehicle (CV) networks have emerged as key enablers of next-generation intelligent transportation systems, improving road safety and operational efficiency through real-time data exchange. However, large-scale adoption remains limited due to two funda…
  datacite Khandakar, Amith 2026 置信度 0.66
• Compositional and adaptive visual recognition under domain shift
  Human vision is remarkably robust, data-efficient, and adaptive. People can recognize objects across changes in style, viewpoint, lighting, modality, and context, often from only a few examples. Modern deep learning systems, despite their impressive performanc…
  datacite Radwan, Ahmed 2026 置信度 0.66
• FusionNet Lite: Lightweight Sequence Fusion for Predictive Maintenance in Industrial IoT
  Reproducibility capsule for FusionNet Lite, a lightweight sequence-fusion deep-learning pipeline for predictive maintenance in industrial IoT systems. The campaign evaluates FusionNet Lite alongside CNN, BiLSTM, MLP, and CNN-LSTM baselines using centralized an…
  datacite Aman Sharma, Kwan Yong Sim, Siva Chandrasekaran 2026 置信度 0.66 CapsuleEngineeringpredictive-maintenanceindustrial-iotedge-ai
• Artificial Intelligence (AI)- based Energy Efficiency Management for Ships at Berths
  The rapid transformation of global energy systems toward low-carbon and decentralized architectures has introduced unprecedented complexity in planning, operation, and control. Large-scale integration of renewable energy sources, electric vehicles, distributed…
  datacite Tien Anh Tran 2026 置信度 0.66
• Artificial Intelligence (AI)- based Energy Efficiency Management for Ships at Berths
  The rapid transformation of global energy systems toward low-carbon and decentralized architectures has introduced unprecedented complexity in planning, operation, and control. Large-scale integration of renewable energy sources, electric vehicles, distributed…
  datacite Tien Anh Tran 2026 置信度 0.66