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• Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19
  preprints 2020 置信度 0.74
• Leveraging Artificial Intelligence and Data Science Techniques in Harmonizing, Sharing, Accessing and Analyzing SARS-COV-2/COVID-19 Data in Rwanda (LAISDAR Project): Study design and rationale
  preprints 2022 置信度 0.74
• A Blockchain-Based Framework for Covid-19 Detection Using Stacking Ensemble of Pre-Trained Models
  preprints 2023 置信度 0.74
• Algorithms in Future Insurance Markets
  preprints 2020 置信度 0.74
• Federated Learning used for predicting outcomes in SARS-COV-2 patients
  preprints 2021 置信度 0.74
• Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic
  preprints 2023 置信度 0.74
• Resilience, Protagonism, and Readiness of Brazilian Academic Research to Face the COVID-19 Pandemic
  preprints 2023 置信度 0.74
• A Deep Learning Approach of Blood Glucose Predictive Monitoring for Women with Gestational Diabetes
  preprints 2023 置信度 0.74
• An Intelligent Computational Model with Dynamic Mode Decomposition and Attention Features for COVID-19 Detection from CT Scan Images
  preprints 2023 置信度 0.74
• Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions.
  Machine learning (ML) is transforming cancer research and care by enabling analysis of complex, high-dimensional datasets spanning genomics, transcriptomics, proteomics, imaging, and clinical records. By improving risk stratification, accelerating detection an…
  pubmed Yadav K, Gupta T 2026 置信度 0.82
• Selecting medical research data platforms for translational biomedical research: a five-tier overview and requirement-weighted assessment framework.
  Translational biomedical research is increasingly collaborative and multimodal, making secure, high-quality data capture, curation, and analytics a major challenge. This work aims to provide an overview of existing medical research data platforms to support in…
  pubmed Jacobs M, Goudarzi S, Stücke J, Röhm R 等 2026 置信度 0.82
• Exploring Deep Transfer Learning for Medical Image Processing and Analysis: A Comprehensive Analysis across Modalities.
  In recent years, medical imaging has become an important tool for diagnosing diseases and disorders in healthcare. Advanced imaging technologies are being developed for non-invasive and early detection of diseases and disorders. Analyzing medical images by cli…
  pubmed Banu MAS, Dhavapandiammal A, Palanisamy K 2026 置信度 0.82
• Federated learning with noisy labels: A comprehensive and concise review of current methodologies and future directions.
  europepmc 2026 置信度 0.80
• The Continuity Trap in Data Science Health Research.
  Secondary use is now the ordinary condition of data science health research rather than an exception to it. Electronic health records collected for clinical care become prediction tools and inputs for generative AI; imaging archives become foundation-model cor…
  pubmed Adebamowo C, Adebamowo SN, Akintola A, Ikhane P 等 2026 置信度 0.82
• From Algorithmic Performance to Clinical Translation: Translational Readiness of Imaging-Based Artificial Intelligence in Dentistry-A Systematic Review.
  Artificial intelligence is increasingly applied to dental imaging, yet favorable internal performance does not necessarily indicate clinical transferability. This systematic review evaluated whether imaging-based dental artificial intelligence models have prog…
  pubmed Ardila CM, Vivares-Builes AM, Pineda-Vélez E 2026 置信度 0.82
• Artificial intelligence in onco-anaesthesia: Current applications, challenges, and future directions.
  Artificial intelligence (AI) is transforming onco-anaesthesia by shifting practice from reactive physiological management toward predictive and precision-based care. This review outlines current AI applications across the perioperative cancer pathway. Preopera…
  pubmed Sirohiya P, Maurya P, Gupta N, Ratre BK 等 2026 置信度 0.82
• Machine learning for precision obstetrics: A comprehensive review of risk prediction models for hypertensive disorders and pregnancy-related syndromes.
  Gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), preterm birth, and intrauterine growth restriction represent major contributors to maternal and neonatal morbidity worldwide. Traditional screening methods relying on single biomar…
  pubmed Xie J, Shi C, Dou L, Peng D 等 2026 置信度 0.82
• Artificial Intelligence for Intelligent and Secure Digital Services: Integrating AI Chatbots and Fraud Detection Systems
  europepmc 2026 置信度 0.80
• Advancing cancer drug discovery through the integration of machine learning and high-throughput screening.
  Cancer drug discovery is a complex process that requires identifying compounds that selectively target malignant cells. While high-throughput screening (HTS) is essential for testing large libraries, it generates vast datasets that are difficult to interpret. …
  pubmed Herbetko K, Mikołajek M, Wojdyło L, Klasen K 等 2026 置信度 0.82
• FedIHRAS: A Privacy-Preserving Federated Learning Framework for Multi-Institutional Collaborative Radiological Analysis with Integrated Explainability and Automated Clinical Reporting.
  europepmc 2026 置信度 0.80
• The historical evolution, technological transformation, and future vision of personalized medicine: a narrative review.
  The paradigm of personalized medicine is rapidly shifting from traditional, evidence-based genomics to advanced, data-driven ecosystems. Understanding this transition, supported by the computational tools of precision medicine, is critical for managing high-di…
  pubmed Karaismailoğlu R, Bozbuğa N 2026 置信度 0.82
• A Comprehensive Review of Artificial Intelligence-Driven Health Management of Electrical Machines
  europepmc 2026 置信度 0.80
• Artificial intelligence for risk prediction in atherosclerotic cardiovascular disease: A narrative review of advances, validation challenges, and clinical translation (2020-2026).
  Since 2020, artificial intelligence (AI) has been increasingly applied to atherosclerotic cardiovascular disease (ASCVD) risk prediction. This structured narrative review with systematic evidence mapping summarizes literature (2020-2026) examining study design…
  pubmed Liu R, Arena R, Vasile VC, Arruda-Olson AM 等 2026 置信度 0.82
• A clinically grounded taxonomy and systematic review of artificial intelligence for cardiovascular diagnosis: From machine learning to multimodal and agentic systems.
  Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, creating an urgent need for accurate, trustworthy, and clinically deployable artificial intelligence (AI) systems capable of supporting complex diagnostic decision-making. Although…
  pubmed Rezaei Z, Amini MA, Banad YM 2026 置信度 0.82
• Fairness in Federated Medical Imaging: A Systematic Review Through the Dual Fairness Lens
  europepmc 2026 置信度 0.80
• Melanoma Detection Using Deep Convolutional Neural Networks: Architectures, Datasets, and Performance
  europepmc 2026 置信度 0.80
• AI-driven technological breakthroughs and practical pathways for biodiversity conservation and ecological management.
  Global biodiversity decline demands advanced technological solutions. This review synthesizes artificial intelligence (AI)-driven methodological innovations across data, algorithmic, and system layers for biodiversity conservation. At the data layer, multimoda…
  pubmed Guan S, Liao Z, Han X, Niu S 2026 置信度 0.82
• Artificial Intelligence Techniques in Cardiac Neuromodulation: Mechanisms, Applications, and Pathways to Clinical Translation.
  Cardiac neuromodulation includes various methods, such as vagus nerve stimulation, baroreflex activation therapy, renal denervation, and stellate ganglion intervention, and targets the autonomic imbalance contributing to the pathophysiology of many cardiovascu…
  pubmed Pandey K, Pandey P, Khanal S, Panday A 2026 置信度 0.82
• Advances in AI for detecting pulmonary inflammation and perioperative medicine: a mini-review.
  With increasing human longevity, early recognition and treatment of pneumonia in the elderly are crucial to prevent disease progression. Artificial intelligence (AI) is rapidly transforming the detection and management of pulmonary inflammation (pneumonia, COV…
  pubmed Huang K, Liang X, Pi R, Dai J 等 2026 置信度 0.82
• Integration of multi-omics and artificial intelligence for therapeutic insights in Alzheimer's disease: A comprehensive review.
  Alzheimer's disease (AD) is a progressively worsening type of brain disorder that damages the nerve cells. It is marked by the buildup of amyloid-β plaques outside the cells, tau neurofibrillary tangles inside the cells, and overall molecular-level dysfu…
  pubmed Periyasamy TS, Sekar N, Lakshmanan H 2026 置信度 0.82
• Artificial intelligence in oncology: empowering clinicians for responsible integration.
  Artificial intelligence (AI) is rapidly reshaping oncology, from diagnosis to treatment planning and clinical research. This perspective defines the oncologist in the era of AI as a clinician able to critically interpret, supervise, and communicate AI outputs,…
  pubmed Colliver E, Parisini E, Arefaine B, PIVOT team† 等 2026 置信度 0.82
• Adaptive routing protocol for large-scale power internet of things based on edge computing and federated learning.
  europepmc 2026 置信度 0.80
• Towards a cybersecure and privacy enhanced smart grid: A blockchain enabled federated learning framework.
  europepmc 2026 置信度 0.80
• Research and application of machine learning models based on multimodal big data for precise transfusion management in acute myeloid leukaemia.
  Acute myeloid leukaemia (AML) is a highly heterogeneous haematologic malignancy in which transfusion support represents an essential component of comprehensive patient care. This review aims to provide an updated synthesis of recent progress in the development…
  pubmed Li J, An X, Jing Z, Li Y 2026 置信度 0.82
• A dataset-centric review of IoT and IIoT intrusion detection: realism, evaluation biases, and future research directions.
  The rapid growth of IoT and IIoT expands the cyber-attack surface of interconnected and safety-critical systems, and, as such, IDSs have become a fundamental security mechanism. Although very impressive results have been reported for machine learning and deep …
  pubmed Reddy DS, Kumar KA 2026 置信度 0.82
• An international multi-centre study to develop and validate federated learning-based prognostic models for anal cancer.
  europepmc 2026 置信度 0.80
• Artificial intelligence in pediatric arrhythmias: current landscape, unique challenges, and translational perspectives.
  The interpretation of pediatric electrocardiograms (ECGs) and management of childhood arrhythmias represent specialized clinical disciplines complicated by age-dependent physiological evolution. While artificial intelligence (AI) has transformed adult cardiolo…
  pubmed Jia H, Zhu W, Lv J 2026 置信度 0.82
• Assessment of real-world evidence research competencies in federated research networks: a maternal health fellowship program evaluation.
  Generating real-world evidence (RWE) approaches across federated evidence networks based on real-world data requires learning new methodological skills. However, standardized frameworks for defining and assessing competencies for large-scale RWE research remai…
  pubmed Lee H, Martin B, Creanga AA, Minty E 等 2026 置信度 0.82
• EdgeGuard-AI: Zero-Trust and Load-Aware Federated Scheduling for Secure and Low-Latency IoT Edge Networks.
  europepmc 2026 置信度 0.80
• Targeting Airway Remodeling in Severe Asthma: Is There a Window of Opportunity for Biologic Therapy Predicting Effects Using Causal Artificial Intelligence?
  Airway remodeling is increasingly recognized as a major determinant of asthma progression, fixed airflow limitation, and long-term morbidity, particularly in severe disease. Although biologic therapies have transformed outcomes by reducing exacerbations and sy…
  pubmed Gangemi S, Manti S, Virchow JC, Canonica GW 2026 置信度 0.82
• Machine Learning for Wound Management: A Structured Narrative Review of Applications, Challenges, and Prospects.
  Chronic and complex wounds represent a major clinical and economic burden, requiring frequent assessment, early detection of complications, and accurate prediction of healing outcomes. Conventional wound evaluation is often subjective and time-intensive, motiv…
  pubmed Das IJ, Debata J, Bhatta K, Panda S 等 2026 置信度 0.82
• Distributed Artificial Intelligence for IoT Security: A Structured Review.
  The expansion of the Internet of Things (IoT) has increased the complexity of securing distributed systems against growing threats to data security, privacy, and reliability. Conventional centralised cybersecurity methods are often insufficient for environment…
  pubmed Szymoniak S, Kubanek M 2026 置信度 0.82
• A multi-modal data fusion and real-time monitoring on stroke risk prediction using federated learning.
  Predicting the risk of stroke is one of the critical problems in healthcare, which necessitates efficient solutions for providing accurate and prompt risk assessments while preserving data confidentiality. This work proposes a new framework using Federated Lea…
  pubmed K RS, P MK 2026 置信度 0.82
• Optimization Strategies and Complexity in ML Models
  europepmc 2026 置信度 0.80
• Editorial: Generative AI and large language models in microbial evolution, resistance mechanisms, and antimicrobial drug discovery.
  europepmc 2026 置信度 0.80
• Artificial intelligence-based prognostic modeling in temporomandibular disorders and chronic orofacial pain: a critical conceptual review.
  This conceptual review (ⅰ) analyzes the outcomes predicted, data modalities, modeling approaches, validation strategies, and reporting quality of existing artificial intelligence (AI)-driven prognostic models in temporomandibular disorders (TMD) and chr…
  pubmed Al-Harthy MH 2026 置信度 0.82
• Artificial intelligence in haematology laboratory diagnosis: Current applications, challenges, and future directions.
  Artificial intelligence (AI) is transforming haematology diagnostics by improving accuracy, efficiency, and reproducibility in workflows traditionally reliant on manual microscopy and expert interpretation. Integrating AI into laboratory medicine presents oppo…
  pubmed Osman HA 2026 置信度 0.82
• From subjective assessment to data-driven diagnosis: the AI revolution in multimodal early detection of Sjögren's disease.
  Sjögren's disease (SjD), a chronic autoimmune disorder affecting exocrine glands, faces significant diagnostic challenges due to its highly heterogeneous symptoms, subjective interpretation of imaging findings, and reliance on invasive biopsies, often res…
  pubmed Zhou Y, Yu B, Chang R, Ding Y 等 2026 置信度 0.82
• From severity scoring to predictive analytics: the emerging role of AI in neurosurgery.
  The rapid integration of artificial intelligence (AI) into neurosurgical practice is transforming every phase of patient care from diagnostic imaging and preoperative planning to intraoperative decision-making and postoperative management. This narrative revie…
  pubmed Huang Y 2026 置信度 0.82
• Securing Federated Learning With Blockchain in the Medical Field: Systematic Literature Review.
  pubmed Wang X, Xie Y, Chen X, Yang J 等 2026 置信度 0.82
• Artificial Intelligence Applications in Antimicrobial Resistance: Comprehensive Review of Predictive Models, Diagnostic Innovations, and Clinical Integration.
  Antimicrobial resistance (AMR) represents a critical global health crisis, driving increased mortality, treatment failure, and economic burden. Artificial intelligence (AI) offers transformative potential to counter this threat by enhancing detection, diagnost…
  pubmed Touati A, Boufahja F, Ben Hamadi N, Touaitia R 等 2026 置信度 0.82
• Privacy-Friendly IoT: Does federated learning guarantee privacy?
  Abstract Federated learning is an emerging machine learning paradigm where multiple clients train models locally and formulate a global model based on the local model updates. Privacy is one of its essential properties. To determine if federated learning guara…
  crossref Majid Abdollahi 2023-11-17T20:33:58Z 置信度 0.70
• A Comprehensive Review of Federated Learning Applications in Healthcare
  crossref Nisha Arora 2026-05-16T11:25:58Z 置信度 0.70
• Review for "Compensation Layer‐added Federated Learning Receiver: Design and Implementation"
  crossref 2022-07-18T09:02:57Z 置信度 0.70
• Privatized Graph Federated Learning
  Abstract Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its …
  crossref Elsa Rizk, Stefan Vlaski, Ali H. Sayed 2023-01-31T03:48:46Z 置信度 0.70
• WITHDRAWN: Performance Analysis of Personalized Federated Learning Algorithms for Image Classification
  Abstract The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding au…
  crossref 2023-09-15T08:48:14Z 置信度 0.70
• Review for "Compensation Layer‐added Federated Learning Receiver: Design and Implementation"
  crossref 2022-07-18T09:02:57Z 置信度 0.70
• Review for "Compensation Layer‐added Federated Learning Receiver: Design and Implementation"
  crossref 2022-07-18T09:02:57Z 置信度 0.70
• Review for "Compensation Layer‐added Federated Learning Receiver: Design and Implementation"
  crossref 2022-07-18T09:02:57Z 置信度 0.70
• Federated Learning aided Breast Cancer Detection with Intelligent Heuristic-based Deep Learning Framework
  Abstract Breast cancer is the second largest cause of female cancer death and one of the most hazardous diseases that leads to a higher mortality rate. Breast cancer is initialized with the malignant stage, where the abnormal growth of cancerous lumps is initi…
  crossref atul b kathole 2022-06-16T18:54:15Z 置信度 0.70
• Federated learning for 5G-enabled infrastructure inspection with UAVs
  Abstract Electricity infrastructures include assets that require frequent maintenance, as they are exposed into heavy use, in order to produce energy that satisfies customer demands. Such maintenance is currently performed by specialized personnel that is scaf…
  crossref Alexios Lekidis 2022-09-19T19:44:07Z 置信度 0.70
• A Survey on Federated Learning PoisoningAttacks and Defenses
  Abstract As one kind of distributed machine learning technique, federated learning enables multiple clients to build a model across decentralized datacollaboratively without explicitly aggregating the data. Due to its abilityto break data silos, federated lear…
  crossref Junchuan Liang, Rong Wang 2022-08-05T18:36:58Z 置信度 0.70
• Federated Learning for Collaborative Network Security in Decentralized Environments
  Abstract In decentralized network environments, collaborative efforts are crucial to bol- stering network security against ever-evolving threats from malicious actors. Federated Learning has emerged as a promising solution, enabling multiple nodes to collectiv…
  crossref Bheema Shanker Neyigapula 2023-08-11T00:18:13Z 置信度 0.70
• Towards an Efficient, Privacy-aware Federated Learning Scheme
  Abstract With the rapid evolution of Internet-of-Things (IoT) devices and the emergence of Autonomous Vehicles (AVs), machine learning processes pose a growing privacy issue. Federated learning (FL) and current cryptography can mitigate this problem; however, …
  crossref Levente Alekszejenkó, Tadeusz Dobrowiecki 2022-10-11T13:42:16Z 置信度 0.70
• Provision for Energy: Federated Learning in Edge Systems
  Abstract An energy-efficient transmission and computation resource allocation problem for federated learning (FL) on wireless communication networks is investigated. Based on the considered model, each user exploits limited local computing resources to train a…
  crossref Mingyue Liu, Syazwina Alias 2022-08-16T12:36:15Z 置信度 0.70
• Federated Learning Applications, Challenges & Future Directions – A review
  Federated learning (FL) is a new technology that has been a hot research topic. It enables the training of an algorithm across multiple decentralized edge devices or servers holding local data samples without exchanging them. Federated Learning embodies the pr…
  crossref A. Shubha, A. Kanagaraj 2026-03-30T22:39:52Z 置信度 0.70
• Federated learning for feature-fusion based requirement classification
  Abstract With the increasing complexity of products, the utilization of automated requirements classification in the process of requirement engineering is of positive significance to improve the efficiency of product development. There have been many experimen…
  crossref Ruiwen wang, Jihong Liu 2023-01-20T08:46:49Z 置信度 0.70
• Privacy-Preserving Federated Learning Framework using Permissioned Blockchain
  Abstract Data is readily available with the growing number of smart and IoT devices. Industries of different sectors follow technological advancement to be benefited from data sharing. However, application-specific data is available in small chunks and distrib…
  crossref Harsh Kasyap, Somanath Tripathy 2023-03-10T04:31:07Z 置信度 0.70
• Edge Assignment in Edge Federated Learning
  Abstract Federated Learning (FL) is a recent Machine Learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Because FL depends on a central server for repeated aggregati…
  crossref Thuy Do, Duc A. Tran, Anh Vo 2023-08-23T15:48:15Z 置信度 0.70
• WITHDRAWN: Depression clinical detection model based on social media: a federated deep learning approach
  Abstract The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding au…
  crossref 2023-05-25T11:59:55Z 置信度 0.70
• A Review of Privacy-Preserving Federated Learning for the Internet-of-Things
  crossref Christopher Briggs, Zhong Fan, Peter Andras 2021-06-10T23:42:05Z 置信度 0.70
• Federated Learning Vulnerabilities, Threats and Defenses: A Systematic Review and Future Directions
  crossref Suzan Almutairi, Ahmad Barnawi 2023-01-28T23:27:04Z 置信度 0.70
• A comprehensive experimental comparison between federated and centralized learning
  Abstract Purpose : Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases s…
  crossref Swier Garst, Julian Dekker, Marcel Reinders 2023-10-16T01:52:07Z 置信度 0.70
• Federated Learning and Data Privacy: A Review of Challenges and Opportunities
  crossref Praveen Kumar Myakala, Chiranjeevi Bura, Anil Kumar Jonnalagadda 2024-12-21T02:38:22Z 置信度 0.70
• Content Cooperative Caching in Mobile Edge Network Through Federated Reinforcement Learning
  Abstract Content caching in the mobile edge network can alleviate the backhaul burden and enhance user experience. The edge content caching imposes a significant challenge, due to the limited caching capacity of base stations, content redundancy, low caching u…
  europepmc Jipeng Zhou, Shaomei Lv 2026 置信度 0.80
• Federated-Inspired Vertically Distributed Quantum Machine Learning: Benchmarking and Hardware Evaluation
  Abstract Distributed quantum machine learning (QML) provides a natural framework for studying variational quantum models under distributed and feature-partitioned execution, but the accuracy-practicality trade-offs of vertical partitioning in the near-term reg…
  preprints Stefan Klug, Maximilian Moll 2026 置信度 0.74
• Migrating Federated Learning to Centralized Learning with the Leverage of Unlabeled Data
  Abstract Federated learning carries out cooperative training without local data sharing, the obtained global model performs generally better than independent local models. Benefifiting from the free data sharing, federated learning preserves the privacy of loc…
  crossref Tianqing Zhu, Xiaoya Wang, Wei Ren, Dongmei Zhang 等 2022-04-12T18:34:51Z 置信度 0.70
• A Review of Federated Learning and Its Importance in Advancing Agricultural Practices
  crossref Sugandha Saxena, Mude Nagarjuna Naik, R. Sriramkumar, Joshuva Arockia Dhanraz 等 2026-06-23T12:51:06Z 置信度 0.70
• Location Prediction with Personalized Federated Learning
  Abstract Location prediction has attracted wide attention in human mobility prediction because of the popularity of location-based social networks. Existing location prediction methods have achieved remarkable development in centrally stored datasets. However,…
  crossref shuang wang, Bowei Wang, Shuai Yao, Jiangqin Qu 等 2021-11-11T15:45:01Z 置信度 0.70
• Quantum Federated Learning: A Comprehensive Review and Analysis_supp1-3712717.pdf
  crossref Shiva Raj Pokhrel 2026-07-15T20:16:11Z 置信度 0.70
• Federated Disentangled Representation Learning for Unsupervised Brain Anomaly Detection
  Abstract Recent advances in Deep Learning (DL) and the increased use of brain MRI have provided a great opportunity and interest in automated anomaly segmentation to support human interpretation and improve clinical workflow. However, medical imaging must be c…
  crossref Cosmin Bercea, Benedikt Wiestler, Daniel Rueckert, Shadi Albarqouni 2021-08-06T19:29:51Z 置信度 0.70
• WITHDRAWN: Depression clinical detection model based on social media: a federated deep learning approach
  Abstract Depression can significantly impact people’s mental health, and recent research shows that social media can provide decision-making support for healthcare professionals and serve as supplementary information for understanding patients’ health status. …
  crossref Yang Liu 2023-05-15T18:51:14Z 置信度 0.70
• Secure and Lossless Federated Matrix Learning for Recommender Systems
  Abstract Privacy concerns within recommender systems have emerged as a pivotal challenge, garnering significant scrutiny from both academic and industrial sectors. While federated matrix factorization have been proposed to enhance privacy via local differentia…
  preprints Rongwei Lu, Xuefeng Duan, Guoqiang Deng, Yong Ding 2026 置信度 0.74
• Peer Review #2 of "OES-Fed: a federated learning framework in vehicular network based on noise data filtering (v0.1)"
  crossref 2022-09-25T02:33:01Z 置信度 0.70
• Provision for Energy: A Resource Allocation Problem in Federated Learning for Edge Systems
  Abstract The article explores an energy-efficient method for allocating transmission and computation resources for federated learning (FL) on wireless communication networks. The model being considered involves each user training a local FL model using their l…
  crossref Mingyue Liu, Leelavathi Rajamanickam 2023-04-19T10:44:46Z 置信度 0.70
• Differentially Private Knowledge Transfer for Federated Learning
  Abstract Extracting useful knowledge from big data is important for machine learning. When data is privacy-sensitive and cannot be directly collected, federated learning is a promising option that extracts knowledge from decentralized data by learning and exch…
  crossref Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang 等 2022-05-26T17:51:02Z 置信度 0.70
• Homomorphic Encryption in Federated Learning: A Systematic Review of Current Methods and Open Challenges
  Federated learning (FL) enables collaborative training across different devices or institutions. However, this model also raises concerns about data privacy. For addressing this problem, there are some privacy-preserving technologies being used, such as differ…
  crossref Haowen Li, Zhengyu Li 2025-08-06T12:00:01Z 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
• Peer Review #3 of "OES-Fed: a federated learning framework in vehicular network based on noise data filtering (v0.3)"
  crossref 2022-09-25T02:33:14Z 置信度 0.70
• Federated Transfer Learning for Soalr Flare Forecasting
  Abstract The critical role of solar flare forecasting in shielding space-based systems and essential infrastructures from potential hazards is universally acknowledged. Most research in solar flare forecasting predominantly utilizes machine learning techniques…
  crossref Junfeng Fu, Jie Wan, Xin Huang, Ke Han 等 2023-07-10T06:15:32Z 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
• A Review of Lightweight Multi-Party Computation and Federated Learning in Financial Systems
  crossref Tonsia Treesa Thomas, Heta Shukla 2025-08-27T16:20:10Z 置信度 0.70
• A Review of Lightweight Multi-Party Computation and Federated Learning in Financial Systems
  crossref Tonsia Treesa Thomas, Heta Shukla 2025-08-08T17:38:17Z 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
• Improved Reputation Evaluation for Reliable Federated Learning on Blockchain
  Abstract Worker selection is critical to the success of federated learning, but issues such as inadequate incentives and poor-quality data can negatively impact the process. Existing studies have used the multi-weight subjective logic model, but it is vulnerab…
  crossref Jiacheng Sui, Yi Li, Hai Huang, Li Fang 2023-04-06T14:19:06Z 置信度 0.70
• Peer Review #3 of "OES-Fed: a federated learning framework in vehicular network based on noise data filtering (v0.2)"
  crossref 2022-09-25T02:33:06Z 置信度 0.70
• Peer Review #2 of "OES-Fed: a federated learning framework in vehicular network based on noise data filtering (v0.2)"
  crossref 2022-09-25T02:33:08Z 置信度 0.70
• Quantum Safe Federated Reinforcement Learning for Intelligent Energy Efficiency Optimization in IoT Enabled Smart Grids
  Abstract A smart grid is as an electricity system that manages digital and refined mechanism to monitor and handle the transfer of electricity from various sources, optimizing efficiency and reliability while reducing costs and environmental impacts. The rapid…
  preprints Abdulatif Alabdulatif 2026 置信度 0.74
• A Comprehensive Review on Federated Learning in Disease Detection
  crossref Projesh Saha 2026-04-02T10:35:52Z 置信度 0.70
• Federated Learning for Lung Cancer Detection: Comparative Analysis and Visual Interpretability
  Abstract Artificial Intelligence (AI) has become a crucial tool in the detection of lung can-cer through medical image segmentation. However, traditional AI approaches, which require centralizing sensitive patient data for model training, raise sig-nificant pr…
  crossref Osei isaac, Iven Aabaah, Benjamin Appiah 2025-03-07T06:50:12Z 置信度 0.70