A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Computer Modeling in Engineering & Sciences, Journal Year: 2024, Volume and Issue: 140(3), P. 2239 - 2274

Published: Jan. 1, 2024

Federated learning is an innovative machine technique that deals with centralized data storage issues while maintaining privacy and security.It involves constructing models using datasets spread across several centers, including medical facilities, clinical research Internet of Things devices, even mobile devices.The main goal federated to improve robust benefit from the collective knowledge these disparate without centralizing sensitive information, reducing risk loss, breaches, or exposure.The application in healthcare industry holds significant promise due wealth generated various sources, such as patient records, imaging, wearable surveys.This conducts a systematic evaluation highlights essential for selection implementation approaches healthcare.It evaluates effectiveness strategies field offers analysis domain, encompassing metrics employed.In addition, this study increasing interest applications among scholars provides foundations further studies.

Language: Английский

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

Healthcare, Journal Year: 2024, Volume and Issue: 12(24), P. 2587 - 2587

Published: Dec. 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.

Language: Английский

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Blockchain-Integrated Security for Real-Time Patient Monitoring in the Internet of Medical Things Using Federated Learning

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 117826 - 117850

Published: Jan. 1, 2023

The Internet of Medical Things (IoMT) heralds a transformative era in healthcare, with the potential to revolutionize patient care, healthcare services, and medical research. As all technological progressions, IoMT introduces suite complex challenges, predominantly centered on security. In particular, ensuring integrity, confidentiality, availability health data real-time communication stands paramount, given sensitivity information ramifications breaches or misuse. light these existing security frameworks, while commendable, exhibit limitations. Specifically, they often grapple comprehensive anomaly detection, effective resistance replay attacks, robust protection against threats like man-in-the-middle eavesdropping, tampering, identity spoofing. proposed framework integrates state-of-the-art encryption techniques, cutting-edge pattern recognition modules, adaptive learning mechanisms. These components collaboratively ensure integrity during transmission, provide conventional novel attack vectors, adapt evolving through continuous learning. Moreover, incorporates sophisticated checksum techniques advanced behavioral analysis, further enhancing its protective capabilities. Our system demonstrated significant improvements detection metrics, consistently outperforming benchmark solutions MRMS BACKM-EHA.

Language: Английский

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Detecting Data Poisoning Attacks in Federated Learning for Healthcare Applications Using Deep Learning

Iraqi Journal for Computer Science and Mathematics, Journal Year: 2023, Volume and Issue: 4(4)

Published: Dec. 18, 2023

This work presents a novel method for securing federated learning in healthcare applications, focusing on skin cancer classification. The suggested solution detects and mitigates data poisoning attacks using deep CNN architecture, specifically VGG16. In architecture with ten institutions, the approach ensures collaborative model training while protecting sensitive medical data. Data is meticulously prepared preprocessed Skin Cancer MNIST: HAM10000 dataset. uses VGG16's powerful feature extraction to classify cancer. A robust strategy spotting threats presented study. Outlier detection techniques strict criteria flag andevaluate problematic modifications. Performance evaluation proves model's accuracy, privacy, datapoisoning resilience. research learning-based categorization healthcareapplications that secure accurate. improves diagnostics emphasizesdata security privacy settings by tackling attacks.

Language: Английский

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Infrared spectroscopy based Cordyceps authenticity detection and multi-classification tasks by privacy-preserving federated learning

Microchemical Journal, Journal Year: 2024, Volume and Issue: 199, P. 110029 - 110029

Published: Feb. 1, 2024

Language: Английский

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