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• Clustered Federated Learning: A Review
  Clustered Federated Learning (CFL) has emerged as a powerful extension of traditional federated learning to address the challenges posed by heterogeneous, non-IID data across distributed clients. This chapter provides a comprehensive review of the state-of-the…
  crossref Majid Morafah, Mahdi Morafah 2025-02-28T14:14:25Z 置信度 0.70
• Federated learning for privacy-preserving speech recognition
  crossref Chao-Han Huck Yang, Sabato Marco Siniscalchi 2024-02-23T09:49:54Z 置信度 0.70
• Bridging data privacy and intelligence: the landscape of federated learning
  crossref Dipanwita Thakur, Sajal K. Das 2026-06-12T10:49:31Z 置信度 0.70
• Figure 2: Federated learning design.
  crossref 2025-05-08T04:35:49Z 置信度 0.70
• Figure 5: Federated learning delay.
  crossref 2021-11-22T05:14:05Z 置信度 0.70
• Federated learning applications in 6G communications and smart societies
  crossref Radical Rakhman Wahid, Farag Azzedin 2026-06-12T10:49:31Z 置信度 0.70
• Federated Learning with Partially Class-Disjoint Data
  Essentially, the federation of multiple clients is to acquire more complete information to promote learning a powerful model that recognizes general patterns of wider classes. However, a natural case usually presents in the real-world situation under a large l…
  crossref Ziqing Fan, Jiangchao Yao 2024-12-18T10:27:47Z 置信度 0.70
• Personalized federated learning: theory and open problems
  crossref Canh T. Dinh, Tung T. Vu, Nguyen H. Tran 2024-02-23T09:48:23Z 置信度 0.70
• Federated learning framework for survival analysis in healthcare
  crossref Navid Seidi, Satyaki Roy, Sajal K. Das 2026-06-12T10:49:31Z 置信度 0.70
• Federated Learning
  crossref 2024-02-23T09:42:36Z 置信度 0.70
• Incentive-based federated learning: architectural elements and future directions
  crossref Chanuka A.S. Hewa Kaluannakkage, Rajkumar Buyya 2026-06-12T10:49:31Z 置信度 0.70
• Artificial Intelligence Using Federated Learning
  crossref Manjushree Nayak, Debasish Padhi 2024-11-29T17:15:57Z 置信度 0.70
• Federated Learning for Internet of Things
  crossref V. Raghavendran, M. Sathishkumar 2026-05-08T21:41:08Z 置信度 0.70
• Engineering and Deployment of Federated Learning Systems in Agricultural Supply Chains
  crossref Meena Sharma 2026-06-23T12:51:06Z 置信度 0.70
• Application of Artificial Intelligence-Driven Federated Learning Based on Machine Learning and Deep Learning in Medicine 
  Currently, artificial intelligence (AI) technology is developing rapidly. Machine learning and deep learning are algorithms in the field of AI, and their combined use in federated learning is becoming increasingly common in medical research. The emergence of f…
  crossref Luwei Li 2024-12-06T14:09:44Z 置信度 0.70
• Collaborative Federated Learning in Healthcare Systems
  crossref Bini M Issac, SN Kumar 2023-11-16T11:04:28Z 置信度 0.70
• Figure 1: Federated learning framework.
  crossref 2025-03-28T04:19:22Z 置信度 0.70
• Tree-Based Models in Vertical Federated Learning
  crossref 2024-08-28T10:21:23Z 置信度 0.70
• Privacy Preservation in Federated Learning
  crossref P. Keerthana, M. Kavitha, Jayasudha Subburaj 2024-07-18T08:39:48Z 置信度 0.70
• Vertical federated learning with feature and sample privacy
  crossref Linh Tran, Timothy Castiglia, Stacy Patterson, Ana Milanova 2026-06-12T10:49:31Z 置信度 0.70
• Cyber Security Intelligent Systems Based on Federated Learning
  crossref Hamed Tabrizchi, Ali Aghasi 2025-04-23T18:07:48Z 置信度 0.70
• Federated Learning
  crossref Mei Kobayashi 2025-08-01T12:37:40Z 置信度 0.70
• Federated learning on long-tailed datasets
  crossref Marawan Elbatel, Robert Martí, Xiaomeng Li 2025-03-07T12:49:22Z 置信度 0.70
• The Federated Estimand: Healthcare Federated Learning as Evidence Synthesis
  crossref Narasimha Raghavan Veeraragavan, Paul Lambert, Bjarte Aagnes, Jan Franz Nygård 2026-07-29T19:14:34Z 置信度 0.70
• Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning
  crossref Satwat Bashir, Tasos Dagiuklas, Muddesar Iqbal 2026-07-29T19:11:18Z 置信度 0.70
• Quantum federated learning: architectural elements and future directions
  crossref Siva Sai, Abhishek Sawaika, Prabhjot Singh, Rajkumar Buyya 2026-06-12T10:49:31Z 置信度 0.70
• Decentralized Federated Learning
  The rapid advancement of distributed machine learning has created new opportunities for enhancing data privacy and fostering collaborative intelligence. However, significant challenges remain in achieving scalability, efficiency, security, and trust. This thes…
  crossref Gunwant Singh 2025-10-13T13:24:36Z 置信度 0.70
• Secure Federated Learning Framework for Training Deep Learning Models
  crossref Gitanjali Bhimrao Yadav, Jayashri Bagade 2025-08-01T12:15:01Z 置信度 0.70
• Advancing Healthcare Solutions with Federated Learning
  crossref Amogh Kamat Tarcar 2022-07-07T12:16:52Z 置信度 0.70
• Vertical asynchronous federated learning: algorithms and theoretic guarantees
  crossref Tianyi Chen, Xiao Jin, Yuejiao Sun, Wotao Yin 2024-02-23T09:49:20Z 置信度 0.70
• Introduction to Federated Learning
  crossref Heiko Ludwig, Nathalie Baracaldo 2022-07-07T12:16:52Z 置信度 0.70
• Detailed Exploration of the FedLearn Federated Learning System
  crossref 2024-08-28T10:21:23Z 置信度 0.70
• Introduction to Federated Learning Methods and Classifications
  crossref Shashikiran Venkatesha, Ramanathan Lakshmanan 2023-11-16T11:04:28Z 置信度 0.70
• Fairness-aware federated learning
  crossref Yuxin Shi 2025-08-05T10:05:35Z 置信度 0.70
• Utility-privacy tradeoff in federated learning
  crossref Huzaifa Arif, Alex Gittens, Pin-Yu Chen 2025-03-07T12:49:29Z 置信度 0.70
• User Participation and Incentives in Federated Learning
  crossref Muhammad Ali Zeb, Samina Amin 2024-07-18T08:39:48Z 置信度 0.70
• Enhancing Federated Learning in Healthcare Using Adaptive Genetic Model Aggregation
  crossref K. Jegadeeswari 2026-05-08T21:41:08Z 置信度 0.70
• Applications, Challenges, and Opportunities for Federated Learning in 6G
  crossref Roheen Qamar, Saima Siraj 2024-09-20T17:25:42Z 置信度 0.70
• Personalization in Federated Learning
  crossref Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun 2022-07-07T12:31:57Z 置信度 0.70
• Optimization techniques for federated learning algorithms
  crossref Ferdinand Kahenga, Antoine Bagula, Sajal K. Das, Jovita Mateus 等 2026-06-12T10:49:31Z 置信度 0.70
• Improving performance fairness in federated learning for heterogeneous medical images
  crossref Meirui Jiang, Qi Dou 2025-03-07T12:49:30Z 置信度 0.70
• Federated Learning in the Internet of Medical Things
  crossref S. Sabapathi, N. Vijayalaskhmi, S. Sindhu 2024-05-30T10:30:21Z 置信度 0.70
• AI and federated learning
  crossref Lingala Thirupathi, Sandeep Ravikanti, Vineetha Kaashipaka, Krishna Priya Thammana 2025-03-20T10:28:47Z 置信度 0.70
• Federated Learning for IoT/Edge/Fog Computing Systems
  crossref Balqees Talal Hasan, Ali Kadhum Idrees 2024-05-30T06:30:21Z 置信度 0.70
• Federated Learning Architectures, Opportunities, and Applications
  crossref Pradipta kumar Mishra, Rabinarayan Satapathy, Debashreet Das 2023-11-16T11:04:28Z 置信度 0.70
• Incentive Mechanism for Federated Learning
  crossref Lekha C. Warrier, G. K. Ragesh, Pao-Ann Hsiung 2024-05-30T10:30:21Z 置信度 0.70
• Efficient explainable federated learning
  crossref Yuanyuan Chen 2026-03-02T05:11:06Z 置信度 0.70
• Hyper-parameter optimization in federated learning
  crossref Yi Zhou, Parikshit Ram, Theodoros Salonidis, Nathalie Baracaldo 等 2024-02-23T09:49:22Z 置信度 0.70
• Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating
  crossref 2024-08-28T10:21:23Z 置信度 0.70
• Federated Learning
  crossref Anagha N. Chaudhari, A.A. Hitham Seddig, Roshani Raut, Aliza Sarlan 2025-08-01T12:15:01Z 置信度 0.70
• Learning to Unlearn in Federated Learning
  crossref Yixiong Wang, Jalil Taghia, Selim Ickin, Konstantinos Vandikas 等 2025-01-21T18:22:35Z 置信度 0.70
• PETs in practice: using federated learning to train machine-learning models without centralising data
  crossref Claudine Tinsman, Calum Inverarity, Gefion Thuermer, Neil Majithia 2025-01-29T21:40:07Z 置信度 0.70
• Adapting to decentralization: the evolution of computing paradigms and machine learning in federated learning
  crossref Ciza Thomas, Alka Rachel John 2025-09-12T12:25:54Z 置信度 0.70
• Analyzing Federated Learning From a Security Perspective
  crossref Akarsh K. Nair, Jayakrushna Sahoo, Ebin Deni Raj 2024-05-30T10:30:21Z 置信度 0.70
• A lightweight anonymous authentication scheme for federated learning.
  Federated learning enables collaborative model training between central servers and distributed clients without collecting users' raw sensitive data, which effectively promotes the large-scale deployment of intelligent collaborative services. Considering the h…
  pubmed Wu S, Meng G, Lu L, Dong X 等 2026 置信度 0.82
• Cross-Image Federated Learning for Hyperspectral Image Classification.
  europepmc 2026 置信度 0.80
• Traceability in Federated Learning in Healthcare.
  Federated learning (FL) enables privacy-preserving analytics on distributed healthcare data, but achieving transparency remains a critical challenge for trust and accountability. This scoping review focuses on traceability as a core component of transparency a…
  pubmed Tang FK, Grönke A, Sergei G, Jaberansary M 等 2026 置信度 0.82
• Defensive Discrimination and the Cyber-Bioethics of Federated Learning in Healthcare.
  pubmed Iwasaki M 2026 置信度 0.82
• Gradient-Refined Federated Learning on Head-Tail Imbalanced Data.
  europepmc 2026 置信度 0.80
• Optimized IoT clustering and assignment in semi-synchronous federated learning.
  This study focuses on the important task of optimizing device clustering and assigning them to edge servers, while also implementing data redistribution in hierarchical semi-synchronous federated learning within the realm of advancing edge computing. Our resea…
  pubmed Farajvand H, Derakhshanfard N, Mirzaei A, Ghaffari A 等 2026 置信度 0.82
• Implementing a Governance Framework for Federated Learning.
  Although federated learning is becoming the dominant paradigm in multi-institutional AI research for healthcare, its application in the highly secure hospital domain remains limited. We evaluated the proposed governance framework for federated learning to expl…
  pubmed Grönke A, Jaberansary M, Zoubia O, Vorhagen S 等 2026 置信度 0.82
• Digital Twin-Enabled Dynamic Aggregation for Efficient Federated Learning.
  Federated learning (FL) enables collaborative model training without sharing raw data, but it faces challenges due to client heterogeneity, leading to inefficiency and reduced accuracy. This paper proposes a digital twin (DT)-based dynamic FL aggregation metho…
  pubmed Zhuang W, Wang Y, Wang G 2026 置信度 0.82
• Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data.
  Federated learning is an emerging distributed machine learning framework aimed at protecting data privacy. Data heterogeneity is one of the core challenges in federated learning, which could severely degrade the convergence rate and prediction performance of d…
  pubmed Wang F, Tang H, Li Y 2026 置信度 0.82
• Privacy-aware vaccine recommendation using federated learning and blockchain.
  Suitable vaccines for individuals are suggested by the vaccine recommendation system regarding certain criteria. Nevertheless, the existing studies didn't augment the vaccine recommendation system centered on users' symptoms and medical history among several g…
  pubmed Shinzeer CK, Bhagat A, Kushwaha AS 2026 置信度 0.82
• CASCADENCE: a layered cascade defense mechanism for federated learning.
  This paper aims to enhance the security and robustness of Federated Learning (FL) systems through a multi-layered defense. We address the critical challenge of protecting distributed learning environments from adversarial attacks while maintaining high model p…
  pubmed Hashmi SW, Shukla RM, Bhunia S 2026 置信度 0.82
• Hierarchical Information Embeddings With Neural ODEs for Personalized Federated Learning.
  europepmc 2026 置信度 0.80
• Federated Learning with Global Model Hint for Medical Image Object Detection.
  Building an ideal medical image object detection model often requires sufficient training data, which can be challenging to obtain in practical scenarios. Manual annotation is labor-intensive, and sharing datasets may raise data privacy concerns. Although fede…
  pubmed Xu Z, Zhou G, Zhang H, Yang R 等 2026 置信度 0.82
• Federated Learning for Medical Image Classification: A Comprehensive Benchmark.
  europepmc 2026 置信度 0.80
• Recovering Sensitive Medical Text in Federated Learning.
  Federated Learning (FL) allows institutions to train shared models without exchanging raw data, making it a promising approach for healthcare applications that involve sensitive electronic health records (EHRs). However, despite this distributed design, the gr…
  pubmed El Azzouzi M, Bellafqira R, Coatrieux G, Cuggia M 等 2026 置信度 0.82
• BCAFL: a secure and efficient blockchain framework for asynchronous federated learning.
  Traditional synchronous Federated Learning (FL) is subject to the waiting latency inherent to synchronization mechanisms. Consequently, its convergence rate is constrained by straggler nodes within heterogeneous environments. Asynchronous Federated Learning (A…
  pubmed Yun J, Liu T 2026 置信度 0.82
• Game-Theoretic Optimized Federated Learning for Heterogeneous IoT Object Detection.
  Federated object detection allows distributed IoT cameras to learn a shared detector without exposing raw images. Its performance is limited by non-IID scenes, unequal device resources, intermittent links, and selfish participation. We propose GO-FedDet, a gam…
  pubmed Wang Z, Chen J 2026 置信度 0.82
• Security Analysis of a Federated Learning Framework for Medical Image-to-Image Translation.
  Federated Learning (FL) emerged as a privacy-preserving paradigm for collaborative training of deep learning models across institutions without sharing patient data. This approach has been applied to complex tasks such as medical image-to-image (I2I) translati…
  pubmed Raggio CB, Bucher L, Blanck O, Cicone F 等 2026 置信度 0.82
• Privacy-Preserving Lightweight Federated Learning for Heterogeneous Data in Internet of Medical Things.
  europepmc 2026 置信度 0.80
• Differentially private federated learning for localized control of infectious disease dynamics.
  In times of epidemics, swift reaction is necessary to mitigate epidemic spreading. For this reaction, localized approaches have several advantages, limiting necessary resources and reducing the impact of interventions on a larger scale. However, training a sep…
  pubmed Kerkouche R, Zunker H, Fritz M, Kühn MJ 2026 置信度 0.82
• Defending Patient Privacy in Federated Learning with Shadow Model
  Abstract Federated Learning (FL) has become a approach for training models together in privacy-sensitive domains like healthcare, where sharing of raw data is frequently restricted. But recent research has shown that Gradient Inversion Attacks (GIAs) can use s…
  europepmc Jansi Rani M, Hema Meena R, Joshitha K 2026 置信度 0.80
• Nationwide federated learning for histopathology: secure deployment across Germany behind firewalls.
  Federated Learning (FL) enables collaborative training across institutions without sharing sensitive data, a solution for privacy-preserving AI in medical imaging. However, hospital deployment remains challenging due to strict data protection regulations, hete…
  pubmed Babendererde N, Lemke N, Stieber J, Fuchs M 等 2026 置信度 0.82
• Bridging the Ethical Gap: Reconciling Federated Learning With ICH E6 (R3) Oversight.
  pubmed Ménard T 2026 置信度 0.82
• HCFL: hybrid contribution-driven federated learning for fair and efficient optimization.
  Federated Learning (FL) enables collaborative model training across decentralized data silos while preserving data privacy. However, client selection strategies in conventional FL processes typically rely on single-dimensional evaluation metrics, which fail to…
  pubmed Jeong Y, Lee S, Lee J, Choi WG 2026 置信度 0.82
• Whose Federation? Epistemic Closure, Community Exclusion, and the Political Economy of Federated Learning.
  pubmed Acevedo H, Al-Louzi R, Celi LA, Chowdhury M 等 2026 置信度 0.82
• HYPERBOLIC MODEL AGGREGATION FOR FEDERATED LEARNING IN FMRI.
  The privacy-sensitive nature of clinical data often limits the use of machine learning in medical imaging applications, particularly for modalities with high acquisition costs such as functional MRI (fMRI). Federated learning mitigates data-sharing barriers by…
  pubmed Wang J, Dvornek N, Duan P, Marshall A 等 2026 置信度 0.82
• A Bayesian Framework for Clustered Federated Learning.
  europepmc 2026 置信度 0.80
• Federated Learning in Endodontics: A Framework for Privacy-Preserving Multicentre Artificial Intelligence.
  High-quality artificial intelligence (AI) models in endodontics require access to diverse, well-annotated datasets. This review introduces federated learning (FL) as a privacy-preserving framework for collaborative AI in endodontics.
  pubmed Turky M, Samaranayake L, Osathanon T, Dummer PMH 2026 置信度 0.82
• Federated learning and digital twins for lifecycle optimization in Urban building renewal.
  The restructuring of aging Chinese city infrastructure requires new approaches based on computational intelligence and optimization lifecycle structures. Current building renovation methods are limited by the lack of seamless linkage between real-time operatio…
  pubmed Zaofei J, Liao F, Metwally ASM 2026 置信度 0.82
• Quantum Federated Learning in Healthcare: The Shift From Development to Deployment and From Models to Data.
  europepmc 2026 置信度 0.80
• Privacy-preserving Online Federated Learning for Massive Infinite Streams.
  Online federated learning (OFL) is essential for privacy-preserving collaborative online analytics over decentralized streams. Different from batch-based FL, OFL faces new challenges including longitudinal privacy leakage, and accumulated utility loss and comm…
  pubmed Shi L, Ren X, Yang S, Zhao C 等 2026 置信度 0.82
• Performance of federated learning models in health services research: A systematic review and meta-analysis.
  This study aimed to assess the performance of federated learning (FL) models and compare their performance with local and centralized models.
  pubmed Wu G, Yang F, Wu Q 2026 置信度 0.82
• A Lightweight Privacy-Preserving Federated Learning Framework for Heterogeneity-Resilient Skin Cancer Diagnosis.
  europepmc 2026 置信度 0.80
• A Multi-Lingual Cyberbullying Detection and Blockchain-Integrated Privacy-Preserving Federated Learning Framework
  europepmc 2026 置信度 0.80
• Adaptive Client Selection in Tangle-Enabled Federated Learning for Efficient and Traceable Training
  europepmc 2026 置信度 0.80
• Comparative Performance Evaluation of Federated Learning Frameworks for Edge-Oriented 6G Applications
  europepmc 2026 置信度 0.80
• Many Hands, But Who Is In Control? Two Types of Control Gaps in Federated Learning for Healthcare.
  pubmed Drogt J, van der Wal M, Jongsma K 2026 置信度 0.82
• Resource management for blockchain enhanced federated learning in wireless edge networks.
  Though machine learning is widely used in wireless edge networks, the transmission of raw data still suffers from security and privacy leakage. Federated learning (FL) addresses these privacy concerns by enabling model training without sharing raw data. Howeve…
  pubmed Yang Z, Qi W, Guo L 2026 置信度 0.82
• Privacy-preserving clustered federated learning via differential privacy and homomorphically encrypted prototypes.
  Clustered federated learning (CFL) is an effective paradigm for handling statistical heterogeneity by grouping clients with similar data characteristics and learning cluster-specific models. However, existing CFL methods often expose sensitive clustering signa…
  pubmed Zhan J, Jiang Z, Liu L 2026 置信度 0.82
• On the Interaction Between Personalization and Optimization in Federated Learning for Medical Image Classification
  Abstract Federated learning (FL) has emerged as a promising paradigm for privacy-preserving medical image analysis, enabling collaborative model training across distributed institutions without sharing sensitive patient data. However, two key challenges remain…
  europepmc Rimsha Ansar, Zainab Salma, Raquel Hijón Neira 2026 置信度 0.80
• Co-Boosting++: Coupled Optimization of Data and Ensemble for One-Shot Federated Learning.
  europepmc 2026 置信度 0.80
• FuzzyFed-CNN: secure and explainable multimodal federated learning for early Alzheimer's diagnosis.
  Alzheimer's disease (AD) is a progressive neurodegenerative condition that has a great effect on cognitive impairment and quality of life. Timely intervention requires the early and reliable diagnosis of the patient, but current diagnostic systems are frequent…
  pubmed Mohanraj S, Radhakrishnan S 2026 置信度 0.82
• Federated Learning Authenticity Standard for Healthcare as derived from lessons in self-driving cars.
  In this Perspective, we highlight a critical mislabeling problem in healthcare federated learning research. Although federated learning is widely promoted as a privacy-preserving approach for multi-institutional artificial intelligence development, most publis…
  pubmed Santos R, Keane PA 2026 置信度 0.82
• Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering.
  Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients. However, FL suffers performance degradation from heterogeneous and limited data. To alleviate the degradation, we present a novel…
  pubmed Zhang X, Li W, Shao Y, Liu Y 等 2026 置信度 0.82
• Privacy-Preserving Federated Learning for Artistic Image Analytics in Visual IoT Sensor Networks
  europepmc 2026 置信度 0.80
• Sample-Level Prototypical Federated Learning.
  europepmc 2026 置信度 0.80