Federated Learning paradigm in E-health systems: An overview

Sellamna, Abdellatif; Boukhamla, Akram Zine Eddine; Benkaddour, Mohammed Kamel

doi:10.1109/pais56586.2022.9946905

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…In this subsection, we provide a brief overview of the most recent research endeavors regarding FL techniques' role in telemedicine from data privacy and decision-making perspectives. The authors in [12] constructed a mechanism to utilize the FL approach in e-health for data security and latency. The proposed FL mechanism's effectiveness was proven over traditional data security techniques.…”

Section: Applications In Telemedicinementioning

confidence: 99%

A Federated Learning Approach to Support the Decision-Making Process for ICU Patients in a European Telemedicine Network

Paragliola,

Ribino,

Ullah

2023

JSAN

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A result of the pandemic is an urgent need for data collaborations that empower the clinical and scientific communities in responding to rapidly evolving global challenges. The ICU4Covid project joined research institutions, medical centers, and hospitals all around Europe in a telemedicine network for sharing capabilities, knowledge, and expertise distributed within the network. However, healthcare data sharing has ethical, regulatory, and legal complexities that pose several restrictions on their access and use. To mitigate this issue, the ICU4Covid project integrates a federated learning architecture, allowing distributed machine learning within a cross-institutional healthcare system without the data being transported or exposed outside their original location. This paper presents the federated learning approach to support the decision-making process for ICU patients in a European telemedicine network. The proposed approach was applied to the early identification of high-risk hypertensive patients. Experimental results show how the knowledge of every single node is spread within the federation, improving the ability of each node to make an early prediction of high-risk hypertensive patients. Moreover, a performance evaluation shows an accuracy and precision of over 90%, confirming a good performance of the FL approach as a prediction test. The FL approach can significantly support the decision-making process for ICU patients in distributed networks of federated healthcare organizations.

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Section: Applications In Telemedicinementioning

confidence: 99%

A Federated Learning Approach to Support the Decision-Making Process for ICU Patients in a European Telemedicine Network

Paragliola,

Ribino,

Ullah

2023

JSAN

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“…Additionally, the significance of feature learning is emphasized. DL is rapidly evolving in medical as well as health field and is widely utilized in image as well as voice processing [6].…”

mentioning

confidence: 99%

Machine learning-based D2D communication for a cloud-secure e-health system and data analysis by feature selection with classification

Awasthi

Suchithra

Chakravarty

et al. 2023

Preprint

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Numerous aspects of healthcare have been altered by cloud-based computing. Scalability of required service as well as ability to upscale or downsize data storage, as well as the collaboration between AI and machine learning, are main benefits of cloud computing in healthcare. Current paper looked at a number of different research studies to find out how intelligent techniques can be used in health systems. The main focus was on security and privacy concerns with the current technologies. This study proposes a novel method for cloud service device-to-device communication using feature selection and classification for data analysis in an e-health system. Through a comprehensive requirement analysis as well as user study, the purpose of this research is to investigate viability of incorporating cloud as well as distributed computing into e-healthcare. After that, the smart healthcare system and conventional database-centric healthcare methods will be compared, and a prototype system will be created as well as put into use based on results. Convolutional adversarial neural networks with transfer perceptron are used to analyze the cloud-based e-health data that has been collected. Proposed technique attained training accuracy 98%, validation accuracy 93%, PSNR 66%, MSE 68%, precision 72%, QoS 63%, Latency 58%.

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Machine learning-based D2D communication for a cloud-secure e-health system and data analysis by feature selection with classification

Awasthi,

Suchithra,

Chakravarty

et al. 2023

Soft Comput

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Federated Learning paradigm in E-health systems: An overview

Cited by 3 publications

References 19 publications

A Federated Learning Approach to Support the Decision-Making Process for ICU Patients in a European Telemedicine Network

A Federated Learning Approach to Support the Decision-Making Process for ICU Patients in a European Telemedicine Network

Machine learning-based D2D communication for a cloud-secure e-health system and data analysis by feature selection with classification

Machine learning-based D2D communication for a cloud-secure e-health system and data analysis by feature selection with classification

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