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• Precision immuno-oncology in oral cancer: latest trends in biomarkers, novel drug development and nanoparticle-based therapeutic platforms.
  Oral squamous cell carcinoma poses a significant global health burden, with over 370,000 annual cases and poor 5-year survival rates of 50%-60%, driven by risk factors like tobacco and alcohol. Despite advances in surgery, radiotherapy, and chemotherapy, funct…
  pubmed Augustine D, Sowmya SV, Pushpalatha C, Prasad K 等 2026 置信度 0.82
• Unlocking multi-institutional insights into disease progression with PEAL as a lossless, one-shot federated learning solution.
  Multisite analysis of electronic health record (EHR) data presents unique opportunities for studying disease progression in real-world settings. However, privacy concerns, communication costs, and site-level heterogeneity pose significant challenges for analyz…
  pubmed Shen Y, Kim JS, Luo C, Zeger SL 等 2026 Jun 18 置信度 0.82
• Enhancing X-ray Image Classification through Heterogeneous Federated Learning with Natural Image-Augmented Models.
  Deep learning-based computer-aided diagnosis (DL-CAD) models have achieved remarkable success in X-ray image analysis. Yet their effectiveness is often constrained by the tight regulations of governing sensitive X-ray data. Federated Learning (FL) offers a pro…
  pubmed Hu Y, Huang YA, Liu R, Xue X 等 2026 Jun 17 置信度 0.82
• Artificial intelligence in thoracic surgery: a narrative review of clinical advances and applications in 2025.
  The integration of artificial intelligence (AI) into thoracic surgery accelerated notably over the course of 2025, transitioning from isolated diagnostic aids toward comprehensive clinical pathway integration. The objective of this narrative review is to synth…
  pubmed Zhang Y, Yang Z, Lin Y, Zhao Y 等 2026 May 31 置信度 0.82
• Advances in AI-based diagnosis of Alzheimer's disease using MRI: a comprehensive survey.
  Artificial intelligence (AI), especially Deep Learning (DL), has been shown significant in accelerating the detection and diagnosis of neurological disorders via medical imaging. This study is mainly focused on Alzheimer's disease (AD), which reveals distincti…
  pubmed Alyaqoobi HIR, Lopez-Guede JM, Dara OA, Ramos-Hernanz JA 等 2026 置信度 0.82
• Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.
  Learning-based image classification has become central to modern medical imaging, but the field is changing rapidly: foundation models, vision-language models (VLMs), and label-efficient pretraining are reshaping which methods are clinically useful. This revie…
  pubmed Ghaffar Nia N, Manwar R, Avanaki K 2026 Jun 16 置信度 0.82
• Nanostructured interfaces integrated with unsupervised intelligence to mitigate global polycrisis complexities.
  Nanostructured sensors are increasingly deployed to mitigate the complexities of the global polycrisis, including climate instability, antimicrobial resistance, pandemics, and emerging technological disruptions. While advanced nano-interfaces (such as MXenes, …
  pubmed Chaudhary V, Saichaemchan S, Bhadola P, Kaushik A 2026 Oct 置信度 0.82
• GEN-Guard: correcting generalization failures for deployable federated surgical AI.
  Federated Learning (FL) in surgical video AI enables collaborative model training without sharing sensitive data. However, standard evaluation practices-selecting the "best" global model based only on validation data from participating hospitals-can lead to su…
  pubmed Alekseenko J, Mascagni P, AI4SafeChole Consortium, Padoy N 2026 Jun 14 置信度 0.82
• Margin-aware prototype learning for client withdrawal in federated unlearning.
  Federated client withdrawal requires removing targeted clients' influence from a collaboratively trained model while preserving utility for remaining participants. Existing approaches face a hard trade-off. To achieve efficiency, they often rely on stored hist…
  pubmed Qiu Y, Shen S, Zhang C, Yue L 等 2026 Jun 6 置信度 0.82
• Artificial intelligence-powered cloud security strategies for protecting critical clinical operations in healthcare environments.
  The increasing use of cloud computing in hospitals, telemedicine, the Internet of Medical Things (IoMT) and real-time patient monitoring has made for an increasing trend of artificial intelligence-driven cloud security in hospitals. The growing reliance on dis…
  pubmed Dixit RS, Choudhary SL, Arya N, Nathani N 等 2026 Jun 12 置信度 0.82
• FSCL-BC: Federated supervised contrastive learning for breast cancer diagnosis with high sensitivity.
  Accurate diagnosis of breast cancer in dense breasts requires expert radiologists to examine multiple ultrasound images per patient. This diagnosis procedure is tedious, time-consuming, and prone to misdiagnosis due to human fatigue. AI-aided diagnosis systems…
  pubmed Ahmed F, Sánchez D, Haddi Z, Domingo-Ferrer J 2026 置信度 0.82
• FEDERATED LEARNING OF ROBUST INDIVIDUALIZED DECISION RULES WITH APPLICATION TO HETEROGENEOUS MULTIHOSPITAL SEPSIS POPULATION.
  Sepsis is a life-threatening condition affecting millions of individuals in the U.S. each year. The complexity of sepsis clinical management makes individualized treatment approaches desirable. The University of Pittsburgh Medical Center (UPMC) has collected e…
  pubmed Chen X, Talisa VB, Tan X, Qi Z 等 2025 Jun 置信度 0.82
• Federated Learning: Approaches, Possibilities, and Challenges
  In this article, Federated Learning (FL) is explored with an emphasis on methods, practical applications, its current trends, issues, and challenges. Although FL can be used in a variety of fields, applying it across business domains comes with its own set of …
  crossref Sarat Chettri 2022-09-17T23:08:52Z 置信度 0.70
• Figure 6: Overview of knowledge distillation methods using federated learning.
  crossref 2025-07-10T08:22:16Z 置信度 0.70
• Secure Federated Learning for Medical Image Classification
  crossref Ramanpreet Kaur, Amandeep Kaur Virk 2026-07-13T08:58:02Z 置信度 0.70
• Figure 1: Proposed federated deep learning framework overview.
  crossref 2024-02-29T04:35:09Z 置信度 0.70
• Federated Learning
  crossref Alexander Jung 2023-01-01T06:15:35Z 置信度 0.70
• Adaptive learning on heterogeneous data in continual federated learning systems
  crossref Alysa Ziying Tan 2026-08-06T04:42:20Z 置信度 0.70
• Video for FedExg: Federated Learning with Model Exchange
  crossref 2020-09-29T09:22:27Z 置信度 0.70
• Federated Learning of Artificial Neural Networks
  crossref Péter Kiss 2023-06-02T12:20:32Z 置信度 0.70
• Enhancing Federated Learning Scalability for Global Agricultural Networks
  crossref Pramod Singh Rathore, Shweta Solanki 2026-06-23T12:51:06Z 置信度 0.70
• Figure 6: Energy consumption during distributed federated learning.
  crossref 2021-11-22T05:14:05Z 置信度 0.70
• FEDND: FEDERATED NEWTON DIRECTION FOR FEDERATED LEARNING ENVIRONMENT
  crossref Samir Ilgar Aliyev 2026-07-27T05:57:16Z 置信度 0.70
• Figure 4: Federated learning with CIFAR-10 dataset.
  crossref 2026-02-04T08:57:09Z 置信度 0.70
• Energy Efficiency IoT Devices in Healthcare Field Using Federated Learning
  crossref T. Kumanan, P. Dineshkumar, M. Sakthivel 2026-05-08T21:41:08Z 置信度 0.70
• Federated, multi-agent, deep reinforcement learning
  Το τοπίο της τεχνητής νοημοσύνης (ΤΝ) αναδιαμορφώνεται από την Ομόσπονδη Μάθηση (ΟΜ), μια αποκεντρωμένη προσέγγιση στη μηχανική μάθηση (ΜΜ) που ενισχύει την ιδιωτικότητα δεδομένων και τη συνεργατική εκπαίδευση μοντέλων. Αυτή η διατριβή εξετάζει τις προκλήσεις …
  crossref Αθανάσιος Ψάλτης 2024-09-20T05:37:31Z 置信度 0.70
• Federated learning in medical image analysis
  crossref Erfan Darzidehkalani 2024-05-14T10:07:57Z 置信度 0.70
• Compression and federated learning : an approach to frugal machine learning
  Compression et apprentissage fédéré : une approche pour l'apprentissage machine frugal Les appareils et outils “intelligents” deviennent progressivement la norme, la mise en œuvre d'algorithmes basés sur des réseaux neuronaux artificiels se développant largeme…
  crossref Louis Leconte 2026-04-08T17:42:37Z 置信度 0.70
• Federated learning in neuroimage segmentation
  Apprentissage fédéré en segmentation en imagerie cérébrale L'apprentissage profond en analyse d'image médicale peut amener des outils cliniques intéressants, en accélérant les tâches rébarbatives et ouvrant la porte à des propositions de diagnostique automatiq…
  crossref Matthis Manthe 2026-04-09T06:58:54Z 置信度 0.70
• Communication Efficient Federated Learning
  crossref Yaochu Jin, Hangyu Zhu, Jinjin Xu, Yang Chen 2022-11-29T19:05:05Z 置信度 0.70
• Cyber resilience through adaptive federated learning
  crossref S. S. Neetha, Singh Shivangi, S. Deepa 2026-03-25T15:39:21Z 置信度 0.70
• Proposed Smart 5G Framework: Incorporating Federated Learning and Transfer Learning
  <p>This research aims to develop a secure and intelligent framework for 5G networks by incorporating federated learning (FL)<br> and transfer learning (TL) strategies. The primary objective is to enhance network evaluation metrics, such as capacity…
  crossref Peyman Khordadpour 2023-05-17T14:37:58Z 置信度 0.70
• Machine Learning for All: A More Robust Federated Learning Framework
  crossref Chamatidis Ilias, Spathoulas Georgios 2019-03-15T11:00:03Z 置信度 0.70
• Vertical Federated Learning
  crossref Qiang Yang, Yang Liu, Yong Cheng, Yan Kang 等 2022-06-09T04:31:06Z 置信度 0.70
• Use-Cases and Scenarios for Federated Learning Adoption in IoMT
  crossref Jonathan Atrey, Ramani Selvanambi 2023-05-03T20:41:25Z 置信度 0.70
• Proposed Smart 5G Framework: Incorporating Federated Learning and Transfer Learning
  This research aims to develop a secure and intelligent framework for 5G networks by incorporating federated learning (FL) and transfer learning (TL) strategies. The primary objective is to enhance network evaluation metrics, such as capacity, service rate, pri…
  crossref Peyman Khordadpour 2023-05-17T10:37:51Z 置信度 0.70
• Tree-Based Models for Federated Learning Systems
  crossref Yuya Jeremy Ong, Nathalie Baracaldo, Yi Zhou 2022-07-07T08:16:52Z 置信度 0.70
• Trusted Federated Learning for Internet of Medical Things
  crossref Sajid Nazir, Yan Zhang, Hua Tianfield 2023-05-03T16:41:25Z 置信度 0.70
• Task-Agnostic Privacy-Preserving Representation Learning via Federated Learning
  crossref Ang Li, Huanrui Yang, Yiran Chen 2020-11-25T20:03:24Z 置信度 0.70
• Horizontal Federated Learning
  crossref Qiang Yang, Yang Liu, Yong Cheng, Yan Kang 等 2022-06-09T00:20:29Z 置信度 0.70
• Framework of Federated Learning and Implementation through Internet of Things
  crossref Deepak Raghava Naik, Kaddour Chelabi, Navya Gubbi Sateeshchandra 2026-05-08T21:41:08Z 置信度 0.70
• Federated learning for predictive modeling of disease prevention in metaverse
  crossref Dharani Jaganathan, A. Vadivel, S. Jansi Rani, Vaishnavi Thangamuthu 2025-09-12T12:25:54Z 置信度 0.70
• Heart Disease Prediction Using Machine Learning and Federated Learning Approach
  crossref Nabiha Fatma, Mohammad Suaib, Jameel Ahmad 2026-05-05T08:01:31Z 置信度 0.70
• Federated Learning: A Hype or a Trend?
  crossref Anna Wilbik 2022-11-14T10:47:20Z 置信度 0.70
• Differential Privacy in Federated Learning
  crossref Shui Yu, Lei Cui 2023-03-26T21:30:41Z 置信度 0.70
• Federated Reinforcement Learning for Portfolio Management
  crossref Pengqian Yu, Laura Wynter, Shiau Hong Lim 2022-07-07T12:16:52Z 置信度 0.70
• Privacy-enhanced DDoS detection with federated learning and differential privacy
  crossref Jovita Mateus, Antoine Bagula, Guy-Alain Lusilao Zodi, Olasupo Ajayi 等 2026-06-12T10:49:31Z 置信度 0.70
• Cybersecurity vulnerabilities in federated learning
  crossref Shaik Valli Haseena, Simna Shanavas, N. Brundha, Ayasha 2026-03-25T15:39:21Z 置信度 0.70
• Federated transfer learning under heterogeneous data
  crossref Xueyang Wu 2023-03-28T05:23:57Z 置信度 0.70
• Federated Learning in Metaverse Healthcare
  crossref 2025-09-13T01:21:32Z 置信度 0.70
• Federated Reinforcement Learning
  crossref Qiang Yang, Yang Liu, Yong Cheng, Yan Kang 等 2022-06-09T04:22:09Z 置信度 0.70
• Federated Transfer Learning
  crossref Qiang Yang, Yang Liu, Yong Cheng, Yan Kang 等 2022-06-09T04:44:22Z 置信度 0.70
• Addressing Trust Issues in Federated Learning
  crossref Dinesh C. Verma 2021-07-13T08:59:18Z 置信度 0.70
• Federated learning for fraud detection and risk mitigation
  crossref Himani Tyagi, Mohit Kumar, Himanshu 2026-03-25T15:39:21Z 置信度 0.70
• Local Training and Scalability of Federated Learning Systems
  crossref Syed Zawad, Feng Yan, Ali Anwar 2022-07-07T12:16:52Z 置信度 0.70
• Privacy-Preserving Vertical Federated Learning
  crossref Runhua Xu, Nathalie Baracaldo, Yi Zhou, Annie Abay 等 2022-07-07T12:16:52Z 置信度 0.70
• Addressing Synchronization Issues in Federated Learning
  crossref Dinesh C. Verma 2021-07-13T08:59:18Z 置信度 0.70
• Navigating the virtual frontier: challenges and solutions for ethical federated learning in metaverse healthcare in India
  crossref Shashwata Sahu, Navonita Mallick 2025-09-12T12:25:54Z 置信度 0.70
• An Introduction to Federated and Transfer Learning
  crossref Roozbeh Razavi-Far, Boyu Wang, Matthew E. Taylor, Qiang Yang 2022-09-30T18:05:01Z 置信度 0.70
• Federated Transfer Reinforcement Learning for Autonomous Driving
  crossref Xinle Liang, Yang Liu, Tianjian Chen, Ming Liu 等 2022-09-30T18:05:01Z 置信度 0.70
• Table 3: Analysis of federated learning in both buildings.
  crossref 2026-02-18T08:00:21Z 置信度 0.70
• Blockchain and federated learning
  crossref Sangeeta Arora, Vivek Tomar, Swati Sharma, Sushil Kumar Narang 2026-03-25T15:39:21Z 置信度 0.70
• A Decentralized Security Paradigm for IoT Ecosystems Using Blockchain Enabled Federated Learning
  crossref Durga Janani C, MUTHUPANDI G 2026-05-03T20:52:58Z 置信度 0.70
• Data poisoning and adversarial attacks in federated learning
  crossref Peer Mohammed Jeelan, K. Nasrulla Khan 2026-03-25T15:39:21Z 置信度 0.70
• Personalized, Robust Federated Learning with Fed+
  crossref Pengqian Yu, Achintya Kundu, Laura Wynter, Shiau Hong Lim 2022-07-07T12:16:52Z 置信度 0.70
• Unifying Intelligence: Federated Learning in Cloud Environments for Decentralized Machine Learning
  crossref Angajala Srinivasa Rao 2024-05-13T04:32:21Z 置信度 0.70
• Empirical Analysis of Federated Learning Algorithms: A Federated Research Infrastructure Use Case
  crossref Harshit Gupta, Abhishek Verma, O. Vyas, Marco Garofalo 等 2023-04-28T17:23:36Z 置信度 0.70
• Performance analysis of federated machine learning frameworks
  crossref Qinghe Jing 2020-03-21T06:44:59Z 置信度 0.70
• Federated learning in the cloud–edge computing continuum: architectures, optimization, and applications
  crossref Fatemeh Mirhakimi, Nan Yang, Rodrigo N. Calheiros, Bahman Javadi 等 2026-06-12T10:49:31Z 置信度 0.70
• Client heterogeneity in federated learning systems
  Hétérogénéité des clients dans les systèmes d'apprentissage fédérés L'apprentissage fédéré (FL) est un cadre collaboratif où les clients (dispositifs mobiles) entraînent un modèle d'apprentissage machine sous la coordination d'un serveur central en préservant …
  crossref Angelo Rodio 2026-04-08T18:28:28Z 置信度 0.70
• CQSA: Byzantine-robust Clustered Quantum Secure Aggregation in Federated Learning
  crossref Arnab Nath, Harsh Kasyap 2026-05-22T12:25:02Z 置信度 0.70
• Figure 2: Architecture of the proposed federated learning system.
  crossref 2026-02-02T08:34:38Z 置信度 0.70
• Efficient communication in federated learning
  crossref Aissa Hadj Mohamed 2025-04-24T08:44:03Z 置信度 0.70
• Federated Learning User Friendly Web App
  crossref Yixuan Wang 2024-06-26T18:23:20Z 置信度 0.70
• Figure 13: Comparison of blockchain-based federated learning models.
  crossref 2026-02-04T08:57:09Z 置信度 0.70
• Synergizing Blockchain and Federated Learning for Scalable , Security and Privacy-Aware IoT Systems.
  crossref MUTHUPANDI G, Vidhya Lakshmi P 2026-05-03T20:52:58Z 置信度 0.70
• Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms
  crossref Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif 2022-09-30T18:05:01Z 置信度 0.70
• Communication-efficient federated learning over wireless networks
  crossref Linping Qu 2024-12-29T23:06:05Z 置信度 0.70
• Algorithm design for communication-efficient federated learning
  crossref Lumin Liu 2024-12-29T22:59:53Z 置信度 0.70
• Federated Edge Learning for Massive MIMO CSI Feedback
  crossref Shi Jin, Yiming Cui, Jiajia Guo 2023-12-02T00:27:27Z 置信度 0.70
• FUBA: Backdoor Federated Learning via Federated Unlearning_supp1-3630110.pdf
  crossref Xinyi Sheng 2025-11-17T18:44:09Z 置信度 0.70
• Robust and Privacy-Preserving Federated Learning
  Vers un Apprentissage fédéré robuste et préservant la confidentialité Dans le monde numérique en perpétuelle mutation d’aujourd’hui, l’apprentissage automatique est désormais une puissance essentielle et révolutionnaire, comme le démontrent de multiples recher…
  crossref Fatima Elhattab 2026-04-08T13:42:09Z 置信度 0.70
• Gradient Leakage and Protection for Federated Learning
  crossref Hanchi Ren 2023-06-15T08:34:11Z 置信度 0.70
• Figure 10: Federated learning with MNIST dataset (no attack).
  crossref 2026-02-04T08:57:09Z 置信度 0.70
• Federated learning techniques in next generation IoT
  Στον σημερινό κόσμο, η συλλογή, η επικοινωνία και η επεξεργασία πληροφοριών αποτελούν τον πυλώνα της σύγχρονης υποδομής που στοχεύει στη δημιουργία ενός καλύτερου περιβάλλοντος και ποιότητας ζωής για τους τελικούς χρήστες. Το Διαδικτύου των Πραγμάτων Νέας Γενι…
  crossref Ηλίας Σινιόσογλου 2025-12-05T13:54:57Z 置信度 0.70
• Tackling heterogeneity in federated learning systems
  Surmonter l'hétérogénéité dans les systèmes d'apprentissage fédéré L'apprentissage fédéré, qui provient de l'anglais ``Federated Learning'' (FL), se présente comme un cadre facilitant l'apprentissage collaboratif de modèles d'apprentissage automatique par des …
  crossref Othmane Marfoq 2026-04-08T10:37:18Z 置信度 0.70
• Algorithm 1 : Federated learning with reputation and CKKS encryption.
  crossref 2025-09-16T08:31:56Z 置信度 0.70
• Figure 11: Federated learning with MNIST dataset (with attack).
  crossref 2026-02-04T08:57:09Z 置信度 0.70
• Federated Learning Framework for Motif Structure Prediction
  crossref Latha Parthiban, G Sivagamasundari, Golda Dilip, A P Venkateswara 等 2026-05-08T21:41:08Z 置信度 0.70
• Table 3: Communication overhead comparison of federated learning methods.
  crossref 2026-06-23T08:58:08Z 置信度 0.70
• Secure Data Aggregation in Federated Learning
  crossref Shui Yu, Lei Cui 2023-03-26T21:30:41Z 置信度 0.70
• Front Matter
  crossref 2026-06-12T10:49:31Z 置信度 0.70
• GAN Attacks and Counterattacks in Federated Learning
  crossref Shui Yu, Lei Cui 2023-03-26T21:30:41Z 置信度 0.70
• Federated Learning for Collaborative Network Security in Decentralized Environments
  In decentralized network environments, collaborative efforts are crucial to bolstering network security against everevolving threats from malicious actors. Federated Learning has emerged as a promising solution, enabling multiple nodes to collectively train ma…
  crossref 2023-10-30T11:24:27Z 置信度 0.70
• Security and Robustness in Federated Learning
  crossref Ambrish Rawat, Giulio Zizzo, Muhammad Zaid Hameed, Luis Muñoz-González 2022-07-07T12:16:52Z 置信度 0.70
• Addressing Data Skew Issues in Federated Learning
  crossref Dinesh C. Verma 2021-07-13T08:59:18Z 置信度 0.70
• Addressing Vertical Partitioning Issues in Federated Learning
  crossref Dinesh C. Verma 2021-07-13T08:59:18Z 置信度 0.70
• Towards Model-Agnostic Federated Learning over Networks
  <p>We present a model-agnostic federated learning method for decentralized data with an intrinsic network structure.The network structure reflects similarities between the (statistics of) local datasets and, in turn, their associated local models. Our me…
  crossref Alexander Jung 2023-02-15T15:53:17Z 置信度 0.70
• Deep learning in digital healthcare: from centralized models to heterogeneity-aware federated learning
  Der Entwicklungsstand der Medizintechnik ist eng mit der menschlichen Gesundheit verknüpft. Zur Beurteilung des individuellen Gesundheitszustands werden vielfältige physiologische Daten mittels medizinischer Geräte erhoben und mit unterschiedlichen Analysemeth…
  crossref Mohan Xu 2026-05-18T12:41:02Z 置信度 0.70
• Poisoning Attacks and Counterattacks in Federated Learning
  crossref Shui Yu, Lei Cui 2023-03-26T21:30:41Z 置信度 0.70