Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration

Healthcare, Год журнала: 2024, Номер 12(24), С. 2587 - 2587

Опубликована: Дек. 22, 2024

Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL's applications within smart health systems, particularly its integration with IoT devices, wearables, remote monitoring, which empower real-time, decentralized data processing for predictive analytics personalized care. It addresses key challenges, including security risks like adversarial attacks, poisoning, model inversion. Additionally, it covers issues related to heterogeneity, scalability, system interoperability. Alongside these, the highlights emerging privacy-preserving solutions, such as differential secure multiparty computation, critical overcoming limitations. Successfully addressing these hurdles essential enhancing efficiency, accuracy, broader adoption in healthcare. Ultimately, FL offers transformative potential secure, data-driven promising improved outcomes, operational sovereignty ecosystem.

Язык: Английский

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Super perfect polarization-insensitive graphene disk terahertz absorber for breast cancer detection using deep learning

Optics & Laser Technology, Год журнала: 2024, Номер 183, С. 112246 - 112246

Опубликована: Дек. 8, 2024

Язык: Английский

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Detecting and classifying breast masses via YOLO-based deep learning

Neural Computing and Applications, Год журнала: 2025, Номер unknown

Опубликована: Март 19, 2025

Язык: Английский

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Artificial intelligence in COVID-19 research: A comprehensive survey of innovations, challenges, and future directions

Computer Science Review, Год журнала: 2025, Номер 57, С. 100751 - 100751

Опубликована: Апрель 4, 2025

Язык: Английский

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TinyVit-LightGBM: A lightweight and smart feature fusion framework for IoMT-based cancer diagnosis

Information Fusion, Год журнала: 2025, Номер unknown, С. 103180 - 103180

Опубликована: Апрель 1, 2025

Язык: Английский

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Decoding the Role of CDCA Genes in Breast Cancer Progression: Insights From in Silico and Functional Assay

Asia-Pacific Journal of Clinical Oncology, Год журнала: 2025, Номер unknown

Опубликована: Апрель 17, 2025

ABSTRACT Background The cell division cycle‐associated (CDCA) genes regulate key cellular processes like cycle progression and division. This study evaluates the diagnostic clinical relevance of CDCA in breast cancer. Methodology Breast cancer normal lines were cultured analyzed for gene expression using RT‐qPCR further validated public databases. Functional assays, including proliferation, colony formation, wound healing, performed following siRNA‐mediated knockdown CDCA2 CDCA3. Mutational, CNV, methylation, survival analyses, along with miRNA regulation PPI network construction, conducted to explore role progression. Results Our findings revealed significant upregulation compared controls, all these exhibiting highest potential based on AUC values ROC analysis. Pathological stage analysis indicated that CDCA5 CDCA7 significantly varied across different stages. Mutational showed had mutation rate, missense mutations being most common. CNV amplification events several genes, particularly CDCA2, CDCA3, CDCA4, CDCA7. Promoter methylation hypomethylation cancer, which correlated negatively their expression. Survival demonstrated high CDCA5, CDCA7, CDCA8 was associated worse overall survival, highlighting prognostic significance. Furthermore, immune infiltration correlations between types, suggesting a modulation. identified specific miRNAs targeting showing as biomarkers. Lastly, CDCA3 cells reduced migration, indicating critical roles tumor growth metastasis. Conclusion highlights promising biomarkers Their correlates poor impairs growth, emphasizing therapeutic targets. These suggest could be integrated into practice improved management. Clinical trial number Not applicable.

Язык: Английский

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Novel Intelligent Exogenous Neuro-Architecture–Driven Machine Learning Approach for Nonlinear Fractional Breast Cancer Risk System

Communications in Nonlinear Science and Numerical Simulation, Год журнала: 2025, Номер unknown, С. 108955 - 108955

Опубликована: Май 1, 2025

Язык: Английский

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Enhanced Machine Learning Models for Accurate Breast Cancer Mammogram Classification

Global Transitions, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

Язык: Английский

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An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 20, 2025

Язык: Английский

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The role of explainable AI in enhancing breast cancer diagnosis using machine learning and deep learning models

Discover Artificial Intelligence, Год журнала: 2025, Номер 5(1)

Опубликована: Май 26, 2025

Язык: Английский

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