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• Learning Paradigms in Cross-Device Federated Recommendation
  crossref Xiangjie Kong, Lingyun Wang, Mengmeng Wang, Guojiang Shen 2025-03-06T01:40:58Z 置信度 0.70
• Improving Healthcare Privacy and Efficiency with Federated Learning
  crossref Dheeraj Sonkhla, Amit Chauhan 2025-09-26T05:44:25Z 置信度 0.70
• Applying Federated Learning to Block-Term Tensor Regression for Decentralised Data Analysis of Biomedical Data
  crossref Axel Faes, Ashkan Pirmani, Yves Moreau, Liesbet M. Peeters 2026-01-20T20:38:34Z 置信度 0.70
• Federated Learning–Based Food Calorie Estimation
  crossref Lingam Sunitha, Shanthi Makka, Kumavat Prakash, Vankadaru Charan 2025-12-15T10:17:52Z 置信度 0.70
• Research on Privacy Protection Technology in Federated Learning
  crossref Zihan Xiang 2025-09-30T10:04:17Z 置信度 0.70
• Review for "IoT-Enabled Electric Load Prediction via Federated Label Distribution Learning"
  crossref 2025-11-18T21:06:43Z 置信度 0.70
• Review for "IoT-Enabled Electric Load Prediction via Federated Label Distribution Learning"
  crossref 2025-11-18T21:06:43Z 置信度 0.70
• Federated Learning
  crossref Harsh Kasyap, Somanath Tripathy, Minghong Fang 2025-11-21T17:09:27Z 置信度 0.70
• Paradigm shift from machine learning to federated learning
  crossref Revathi Vaithiyanathan, Ranjini K. 2024-11-15T04:51:37Z 置信度 0.70
• Review for "IoT-Enabled Electric Load Prediction via Federated Label Distribution Learning"
  crossref 2025-11-18T21:06:43Z 置信度 0.70
• Optimization of Moon Model-Contrastive Federated Learning
  crossref Changyu Chen, Weiheng Rao 2025-09-30T10:59:31Z 置信度 0.70
• Wireless Federated Learning Based Building Temperature Estimation With Latency Constraint
  The paper proposes a novel approach for temperature estimation in buildings using wireless federated learning (FL) while considering latency constraints. The proposed model utilizes a hierarchical federated learning architecture within a wireless network, inco…
  crossref Kemin Zhang 2025-10-16T09:35:03Z 置信度 0.70
• DISTILLED ONE-SHOT FEDERATED LEARNING: A HIGHLY EFFICIENT AND SECURE APPROACH FOR REDUCING COMMUNICATION COSTS IN FEDERATED EDGE LEARNING
  Federated Edge Learning enables dispersed edge nodes to train a global model in the Artificial Internet of Things (AIoT), advancing cloud computing. Nevertheless, existing federated learning techniques suffer from communication inefficiencies, requiring many r…
  crossref R.S. Shudapreyaa, Prasanth T., Vimal M, Raahul Siv V. 等 2026-07-29T13:02:41Z 置信度 0.70
• Federated Diffusion Modeling With Differential Privacy for Tabular Data Synthesis
  crossref Timur Sattarov, Marco Schreyer, Damian Borth 2026-01-20T20:38:34Z 置信度 0.70
• Introduction to Machine Learning
  crossref Somanath Tripathy, Harsh Kasyap, Minghong Fang 2025-11-21T17:09:27Z 置信度 0.70
• Handling Device Heterogeneity in Federated Learning: The First Optimal Parallel SGD in the Presence of Data, Compute and Communication Heterogeneity
  crossref Peter Richtarik 2025-07-15T07:07:04Z 置信度 0.70
• Federated Adaptation of Language Models for On-Device Speech Recognition using Confidence-Aware Training
  crossref Zhe Liu 2026-01-20T20:38:34Z 置信度 0.70
• Cloud Infrastructure and Federated Learning
  crossref Kanishka Gupta, Amit Aylani, Prakash Parmar, Deepak Hajoary 2025-05-27T16:38:31Z 置信度 0.70
• FLASH: A Framework for Federated Learning with Attribute Selection and Hyperparameter Optimization
  crossref Ioannis Christofilogiannis, Georgios Valavanis, Alexander Shevtsov, Ioannis Lamprou 等 2026-01-20T20:38:34Z 置信度 0.70
• Federated Learning for Recommender Systems: Advances and Perspectives
  crossref Vasileios Perifanis, Nikolaos Pavlidis, Andreas Sendros, Pavlos S. Efraimidis 2025-04-26T02:21:18Z 置信度 0.70
• Differentially Private Federated Edge Learning
  crossref Yong Zhou, Wenzhi Fang, Yuanming Shi, Khaled B. Letaief 2025-08-29T12:52:18Z 置信度 0.70
• Scalable Federated Split Learning for Smart Mobile and IoT Devices
  crossref Pham Duy Thanh, Tran Anh Khoa, Minh-Son Dao, Koji Zettsu 2025-12-02T17:01:43Z 置信度 0.70
• Learning Without Sharing: A Comparative Study of Federated Learning Models for Healthcare
  crossref Anja Campmans, Mina Alishahi, Vahideh Moghtadaiee 2025-07-02T04:37:42Z 置信度 0.70
• Federated Bilevel Optimization
  crossref Hongchang Gao 2024-09-03T07:02:43Z 置信度 0.70
• A Threshold-Based Federated Learning Infrastructure for Fintech Services
  Fintech ecosystem has encountered a rapid change in their electronic transactions thereby increasing vulnerabilities during sensitive data transfer. In most cases, the sensitive data is transferred through public channels and sometimes, data gatherers are not …
  crossref Nandan Banerji, Lhamu Sherpa 2025-10-09T17:38:06Z 置信度 0.70
• Proceedings of the Federated Learning and Edge AI for Privacy and Mobility
  crossref 2025-12-02T17:01:43Z 置信度 0.70
• Federated Learning Applications for Urban Intelligence
  crossref Baskar Kasi, Saravanan Ramalingam, T. Sathish Kumar, A. Mohan 2025-10-03T21:19:49Z 置信度 0.70
• Trustworthy Federated Edge Learning via Blockchain
  crossref Yong Zhou, Wenzhi Fang, Yuanming Shi, Khaled B. Letaief 2025-08-29T12:52:26Z 置信度 0.70
• Ensuring data privacy and security in federated learning for healthcare data
  crossref Chinna Ovu Reddi Umarani, Jahangeer Naskath, Nagarajan Karthikeyan, Selvakumar Manickam 2025-03-20T10:28:47Z 置信度 0.70
• Flashback: Understanding and Mitigating Forgetting in Federated Learning
  crossref Mohammed Aljahdali, Ahmed M. Abdelmoniem, Marco Canini, Samuel Horváth 2026-01-20T20:38:34Z 置信度 0.70
• A Review on Detection of Adulteration in Food Using Federated Learning
  crossref Jagamohan Meher, Rajanandini Meher 2025-12-15T10:17:52Z 置信度 0.70
• Fast Decentralized Gradient Tracking for Federated Learning with Local Updates
  crossref Junchi Li 2025-06-13T10:49:46Z 置信度 0.70
• Expanding the federated horizon: cross-domain techniques for collective intelligence
  crossref Vishwa S. Parekh, Pranav Kulkarni, Adway Kanhere, Michael A. Jacobs 2025-03-07T12:49:25Z 置信度 0.70
• Introduction
  crossref Xiaoxiao Li, Ziyue Xu, Huazhu Fu 2025-03-07T12:49:34Z 置信度 0.70
• Introduction
  crossref Xiaoxiao Li, Ziyue Xu, Huazhu Fu 2025-03-07T12:49:14Z 置信度 0.70
• Integrating Blockchain and Federated Learning for Enhanced Security and Privacy in Smart Cities
  crossref Dipali Sarvate, Siddharth Shankar Mishra, V. Shanmugapriya, Dheerendra Panwar 2025-10-03T21:19:49Z 置信度 0.70
• Fin-Fed-OD: Enhancing Outlier Detection Using Federated Learning on Financial Tabular Data
  crossref Dayananda Herurkar, Ahmed Anwar, Sebastian Palacio, Jörn Hees 等 2026-01-20T20:38:34Z 置信度 0.70
• Federated Learning in Brain Tumor Segmentation in Medical Imaging
  crossref Jyoti Kataria, Supriya P. Panda 2025-05-27T16:38:31Z 置信度 0.70
• Federated Learning for Remote Health Monitoring—A Review
  crossref Venkatesh Upadrista, Sajid Nazir, Huaglory Tianfield 2025-09-26T05:55:53Z 置信度 0.70
• Federated Learning for Distributed AI Systems
  crossref Lorna Tobin, Sameera Gallus, Romey Marina 2025-05-02T12:01:28Z 置信度 0.70
• FedSparQ: Adaptive Sparse Quantization with Error Feedback for Robust & Efficient Federated Learning
  crossref Chaimaa MEDJADJI, Sadi ALAWADI, Feras M. Awaysheh, Guilain LEDUC 等 2026-01-20T20:38:34Z 置信度 0.70
• Federated Action Recognition for Smart Worker Assistance Using FastPose
  crossref Vinit Hegiste, Vidit Goyal, Tatjana Legler, Martin Ruskowski 2026-01-20T20:38:34Z 置信度 0.70
• Federated Learning for Predictive Maintenance and Resilience in Urban Infrastructure Systems
  This paper proposes a federated learning-based framework for predictive maintenance and resilience assessment of urban infrastructure systems using multi-source time-series data. The approach enables collaborative model training across distributed edge devices…
  crossref Ammoon Birzoim 2025-06-11T00:35:41Z 置信度 0.70
• Real-Time Threat Detection in Vehicular Networks Using Federated Learning
  crossref Muhammad A 2025-06-25T19:13:54Z 置信度 0.70
• FEDERATED LEARNING FOR DATA PRIVACY AND SECURITY IN INDUSTRIAL APPLICATIONS
  The new opportunities in data privacy and security come with the rapid growth of the Internet of Things (IoT) and Cloud computing in industrial applications. IoT networks, through which physical objects exchange information in real-time, fundamentally alter th…
  crossref Ravikumar Perumallaplli 2025-05-06T16:05:43Z 置信度 0.70
• FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning
  crossref Emmanouil Kritharakis, Antonios Makris, Dusan Jakovetic, Konstantinos Tserpes 2026-01-20T20:38:34Z 置信度 0.70
• Client-Side Patching Against Backdoor Attacks in Federated Learning
  crossref Borja Molina-Coronado 2025-02-10T18:43:50Z 置信度 0.70
• Edge-Efficient Federated Learning Adaptive Compression for Real-Time Robotic Collaboration
  Collaborative robotics demands seamless realtime coordination among distributed agents, yet bandwidth limitations pose significant challenges for federated learning (FL) implementations. This paper presents a novel adaptive compression algorithm tailored for F…
  crossref Hemanth Ravipati 2025-05-27T12:40:33Z 置信度 0.70
• Blockchain-Integrated Federated Learning for IoT-Based Smart Applications
  crossref S. Balakrishnan, Syed Shahul Hameed, RM Sunil Kumar, S. Simonthomas 2025-06-20T18:53:47Z 置信度 0.70
• REVOLUTIONIZING EARLY LUNG CANCER DETECTION WITH MACHINE LEARNING: INSIGHTS FROM FEDERATED AND ENSEMBLE LEARNING
  crossref Jayendra S. Jadhav, Jyoti Deshmukh 2025-12-23T15:22:31Z 置信度 0.70
• Advances in 5G/6G-Enabled Federated Reinforcement Learning in IoT
  crossref P. Arul, V. Yokesh, N. Sathish, Pham Chien Thang 2025-06-20T18:53:47Z 置信度 0.70
• Federated Learning for Plant Disease Detection
  crossref Siddhartha Das, Sudipta Jana, Sudeepta Pattanayak, Pradipta Banerjee 等 2025-12-15T10:17:52Z 置信度 0.70
• Federated Learning for Sustainable Development Using IoT/Edge Computing System
  crossref M. Yogeshwari, S. Lavanya, S. Deepa, Afizan Bin Azman 2025-06-20T18:53:47Z 置信度 0.70
• Front Matter
  crossref 2025-10-03T21:19:49Z 置信度 0.70
• Federated Learning in Smart Agriculture
  crossref Abhishek Tyagi, Shekhar Tyagi, Guru Dayal Kumar 2025-12-15T10:17:52Z 置信度 0.70
• Federated Class-Incremental Learning: A Survey
  crossref Xuefeng Zhu, Liang Bai, Yirun Ruan 2025-10-13T17:39:05Z 置信度 0.70
• Research on Privacy and Security Issues in Federated Learning
  crossref Xinyuan Bi 2025-09-30T10:17:44Z 置信度 0.70
• Collaborative Defense: Federated Learning for Intrusion Detection Systems
  crossref Rawish Butt, Noshina Tariq, Muhammad Ashraf, Mamoona Humayun 等 2025-04-26T02:21:40Z 置信度 0.70
• Federated Learning Frameworks: Privacy-Preserving Optimization for Distributed AI Systems
  The advent of federated learning (FL) has changed the face of collaborative machine learning by facilitating decentralized model training without raw data centralization. The distributed nature of FL brings new challenges in providing privacy, improving learni…
  crossref Vikash Kumar, Krishna Murari 2025-07-29T21:49:56Z 置信度 0.70
• Federated Learning for Cross-Cloud System Migrations: A Privacy-Preserving Approach
  This article presents an innovative approach to SAP ERP system migrations using federated learning frameworks, addressing the critical challenges of data privacy and system availability in cross-cloud migrations. The proposed article integrates
  crossref Krupal Gangapatnam 2025-01-21T14:22:58Z 置信度 0.70
• Use Cases and Scenarios for Federated Learning Adoption in IoT
  crossref Anitha Velu, Raghu Ramamoorthy, A. Prasanth, Ahmed A. Elngar 2025-06-20T18:53:47Z 置信度 0.70
• Federated Reinforcement Learning Framework for Privacy Preserving Few Shot Learning Authors
  This study introduces a federated reinforcement learning framework for few-shot learning (FRL-FSL), aiming to address the dual challenges of data scarcity and privacy preservation in distributed environments. The proposed framework integrates policy gradient o…
  crossref Minsoo Kang, Jihye Park, Donghyun Choi, Seoyeon Kim 2025-09-17T00:50:10Z 置信度 0.70
• A Blockchain-Enabled Federated Neuro-Symbolic Framework for Secure Wearable Biosensor-Based Health Monitoring.
  Wearable biosensors generate continuous physiological data in smart Internet of Disease (IoD) environments. These data can support early disease detection and remote patient monitoring. However, wearable data are often noisy, sensitive, and distributed across …
  pubmed Alshudukhi KS, Tariq N 2026 置信度 0.82
• Advances in artificial intelligence and machine learning for toxicity prediction in computational toxicology: a comprehensive review.
  Over the past three decades, artificial intelligence (AI) and machine learning (ML) have revolutionized computational toxicology, providing powerful tools for predicting chemical toxicity and supporting safer assessments for human health and the environment. T…
  pubmed Shija G 2026 置信度 0.82
• FedMamba-IoMT: Federated state space models with differential privacy and byzantine resilience for privacy-preserving intrusion detection in Internet of Medical Things.
  The proliferation of Internet of Medical Things (IoMT) devices has created critical cybersecurity challenges demanding intrusion detection systems that achieve high accuracy across diverse attack taxonomies while preserving patient privacy across institutional…
  pubmed Al-Sharo YM, Tawfik M, Almadani AM, Abdelhaliem AH 等 2026 置信度 0.82
• Federated ConvNeXt-swin temporal fusion network for malware and botnet detection in IoT systems.
  The rapid expansion of Internet of Things (IoT) infrastructures has significantly increased the exposure of edge devices to malware and botnet attacks. Conventional intrusion detection systems are largely centralized and struggle to operate effectively in dece…
  pubmed Alsubaei FS, Almazroi AA, Ayub N 2026 置信度 0.82
• Correction: Deshmukh et al. Enhancing Privacy in IoT-Enabled Digital Infrastructure: Evaluating Federated Learning for Intrusion and Fraud Detection. <i>Sensors</i> 2025, <i>25</i>, 3043.
  Text Correction [...].
  pubmed Deshmukh A, de la Rosa PE, Rodriguez RV, Dasari S 2026 置信度 0.82
• Centralized pooling and federated learning for Canadian patient-level data sharing in multicenter medical AI: A scoping review.
  Algorithms that support screening, triage, and treatment decisions depend on training data drawn from patient populations. Limited access to patient-level records across institutions and jurisdictions can reduce representation and contribute to uneven model pe…
  pubmed Jafarinezhad O, Zhang Q, Rezai R, Noaeen M 等 2026 置信度 0.82
• Federated learning's uncomfortable truth: why human networks matter more than neural networks.
  To examine real-world barriers to implementing federated learning in healthcare and highlight the organizational, regulatory, and socio-technical factors often overlooked in technical research.
  pubmed Peltonen LM, Chomutare T 2026 置信度 0.82
• Bias, External Validation, and Real-World Implementation of Artificial Intelligence Models in Cardiovascular Medicine.
  Artificial intelligence (AI) and machine learning (ML) have demonstrated strong diagnostic and prognostic performance across cardiovascular medicine. However, translation into equitable real-world benefit is limited by algorithmic bias, inadequate external val…
  pubmed Patel NN 2026 置信度 0.82
• Individualized Per-Site Meta-Federated Feature Learning (iPS-MFFL) for Privacy-Preserving Brain Tumor MRI Classification under non-IID Heterogeneity
  europepmc 2026 置信度 0.80
• Advanced Deep Learning Architectures in MRI-Based Brain Tumor Classification: A Systematic Review Focused on Meningiomas.
  Deep learning (DL) is increasingly applied to automate brain tumor classification from magnetic resonance imaging (MRI), yet meaningful clinical deployment remains limited by tumor heterogeneity, dataset bias, and incomplete tumor-specific validation. This sys…
  pubmed Noor N, Turner C, Holdsworth SJ, Nielsen P 等 2026 置信度 0.82
• FedSynHAR: a framework based on feature-enhanced adaptive pruning-mutual distillation for federated human activity recognition.
  Deep-learning-based human activity recognition (HAR) has been widely studied and applied in recent years, but it raises privacy concerns. Federated learning (FL) enables collaborative training without sharing raw data, thereby protecting user privacy. However,…
  pubmed Wang C, Fan R 2026 置信度 0.82
• From algorithmic innovation to clinical deployment: A systematic review of methodological gaps limiting federated learning in healthcare.
  pubmed Vijayasarathy S 2026 置信度 0.82
• ICDMS: Integrating multimodal data for intelligent clinical decision-making in healthcare: Current trends and future directions.
  Intelligent Clinical Decision-Making Systems have become a cornerstone of modern healthcare by enabling accurate diagnosis, prognosis and treatment planning through data-driven insights. With the growing availability of heterogeneous healthcare data such as me…
  pubmed Shehnaz, Ahamed J, Albaqami AS, Nisa KU 2026 置信度 0.82
• Fed-DTCN: A Federated Disentangled Learning Framework for Unsupervised Zero-Day Anomaly Detection in IoT with Semantic-Aware Augmentation.
  europepmc 2026 置信度 0.80
• Mapping the knowledge landscape and research trends of artificial intelligence in breast cancer diagnosis and treatment: A bibliometric analysis.
  BackgroundArtificial intelligence (AI) has generated rapidly growing research in breast cancer diagnosis and treatment, yet its intellectual structure, collaboration patterns, and thematic evolution remain unmapped.ObjectiveTo analyze the global research lands…
  pubmed Wang C, Hong K, Ying Y, Xu Y 等 2026 置信度 0.82
• AdaFed-LDR: Adaptive Federated Learning with Layerwise Dynamics Regularization for Robust Wi-Fi Localization.
  Wi-Fi Channel State Information (CSI)-based indoor localization enables high-precision positioning, but its deployment across multiple environments faces two major challenges: privacy concerns from centralizing CSI data, and severe statistical heterogeneity (n…
  pubmed Harada K, Natori H, Koike M, Mineno H 2026 置信度 0.82
• Correction: A personalized federated learning-based glucose prediction algorithm for high-risk glycemic excursion regions in type 1 diabetes.
  europepmc 2026 置信度 0.80
• Quantum-enhanced privacy aggregation for healthcare monitoring in wireless body area networks.
  europepmc 2026 置信度 0.80
• Artificial Intelligence-Based Environmental Control Systems for Reducing the Energy Performance Gap in Non-Residential Buildings: A Systematic and Critical Review (2018–2025)
  europepmc 2026 置信度 0.80
• Federated learning with LSTM and error correcting codes for secure and private identification of IoT devices.
  europepmc 2025 置信度 0.80
• A federated transformer-enhanced double Q-network for collaborative intrusion detection.
  With the rapid proliferation of IoT technology, cybersecurity challenges have become increasingly prominent. Traditional centralized network intrusion detection systems (NIDS) exhibit significant limitations including privacy risks and inadequate modeling of s…
  europepmc 2025 置信度 0.80
• A blockchain technology with advanced lightweight symmetric cryptography schemes and smart security framework for healthcare wireless body area networks.
  The rapid evolution of intelligent healthcare systems has brought Healthcare Wireless Body Area Networks (HWBANs) to the forefront of personalized health monitoring. However, current systems face critical challenges, including high computational overhead, cent…
  pubmed Muthupandian S, Kumar DM 2026 置信度 0.82
• Improving Model Fusion by Training-Time Neuron Alignment With Fixed Neuron Anchors.
  europepmc 2026 置信度 0.80
• Correction: Nuclei Segmentation and Classification from Histopathology Images using Federated Learning for End-Edge Platform.
  europepmc 2025 置信度 0.80
• From prediction to practice: artificial intelligence as an enabling layer in liver transplant care - reflections on ILTS 2025.
  Liver transplantation has become increasingly complex and data-intensive, demanding rapid, high-stakes clinical decisions as patient and organ conditions evolve. The surge of multidimensional clinical datasets has sparked strong interest in artificial intellig…
  pubmed Zarrinpar A, Orinion AF 2026 置信度 0.82
• Intrusion Detection Based on Federated Context-Aware Embedded Deep Transfer Learning in Heterogeneous Networks
  Abstract With the continuous advancement of the Internet of Everything paradigm, network intrusion detection systems (IDS) are confronted with multiple challenges in heterogeneous data sharing, integration, and security. To address these issues, this paper pro…
  europepmc Di Chen, Xinpeng Zhang 2025 置信度 0.80
• Collaborative networks, trends, and comparative analysis of artificial intelligence techniques in healthcare research: a narrative review.
  europepmc 2026 置信度 0.80
• LaED: a novel lightweight, edge-aware and explainable deep learning model for privacy-preserving facial attendance tracking in resource-constrained educational environments.
  Facial recognition is increasingly adopted for automated classroom attendance; however, real-world deployment in schools remains constrained by privacy risks, ethical obligations, demographic bias, spoofing threats, and limited computational resources. Recent …
  pubmed Abiodun EO, Abiodun OI, Alawida M, Shawar BA 等 2026 置信度 0.82
• Artificial intelligence in retinal imaging for early Alzheimer's disease detection: A review.
  BackgroundAlzheimer's disease (AD) is a progressive neurodegenerative disorder that necessitates early, accessible, and non-invasive diagnostic methods.ObjectiveThis review examines the potential of artificial intelligence (AI)-based retinal imaging as a trans…
  pubmed Rehman MU, Masip D 2026 置信度 0.82
• Cloud assisted blockchain-enabled split federated learning framework for security and privacy-preserving of IoMT in healthcare 5.0.
  europepmc 2026 置信度 0.80
• Proximal guided hybrid federated learning approach with parameter efficient adaptive intelligence for pneumonia diagnosis.
  europepmc 2025 置信度 0.80
• Optimized attention-based cascaded shuffle long-term dependent network based performance analysis of adaptive e-learning among IT professionals.
  europepmc 2026 置信度 0.80
• dsLassoCov: a federated Lasso approach incorporating covariate control.
  Machine learning has been widely adopted in biomedical research, fueled by the increasing availability of data. However, integrating datasets across institutions is challenging due to legal restrictions and data governance complexities. Federated learning allo…
  pubmed Cao H, Anguita-Ruiz A, Warembourg C, Escribà-Montagut X 等 2026 置信度 0.82
• A data analytics-driven approach to backorder prediction using federated machine learning in industrial supply chains.
  europepmc 2026 置信度 0.80
• Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems.
  europepmc 2026 置信度 0.80
• A Federated Hierarchical DQN-Based Distributed Intelligent Anti-Jamming Method for UAVs.
  europepmc 2025 置信度 0.80
• Federated GenAI with Quantum Optimization for Privacy-Preserving Learning, Knowledge Synthesis, and Scalable Equity in Higher Ed via Decentralized Training
  europepmc 2025 置信度 0.80
• Federated CT Foundation Models for Multi-Center Detection of Lymph Node Metastasis in Pancreatic Cancer
  europepmc 2025 置信度 0.80