Gradient Boosting for Health IoT Federated Learning

Federated learning preserves the privacy of user data through Machine Learning (ML). It enables the training of an ML model during this process. The Healthcare Internet of Things (HIoT) can be used for intelligent technology, remote detection, remote medical care, and remote monitoring. The database...

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Veröffentlicht in:Sustainability 2022-12, Vol.14 (24), p.16842
Hauptverfasser: Wassan, Sobia, Suhail, Beenish, Mubeen, Riaqa, Raj, Bhavana, Agarwal, Ujjwal, Khatri, Eti, Gopinathan, Sujith, Dhiman, Gaurav
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container_end_page
container_issue 24
container_start_page 16842
container_title Sustainability
container_volume 14
creator Wassan, Sobia
Suhail, Beenish
Mubeen, Riaqa
Raj, Bhavana
Agarwal, Ujjwal
Khatri, Eti
Gopinathan, Sujith
Dhiman, Gaurav
description Federated learning preserves the privacy of user data through Machine Learning (ML). It enables the training of an ML model during this process. The Healthcare Internet of Things (HIoT) can be used for intelligent technology, remote detection, remote medical care, and remote monitoring. The databases of many medical institutes include a vast quantity of medical information. Nonetheless, based on its specific nature of health information, susceptibilities to private information, and since it cannot be pooled related to data islands, Federated Learning (FL) offers a solution as a shared collaborative artificial intelligence technology. However, FL addresses a series of security and privacy issues. An adaptive Differential Security Federated Learning Healthcare IoT (DPFL-HIoT) model is proposed in this study. We propose differential privacy federated learning with an adaptive GBTM model algorithm for local updates, which helps adapt the model’s parameters based on the data characteristics and gradients. By training and applying a Gradient Boosted Trees model, the GBTM model identifies medical fraud based on patient information. This model is validated to check performance. Real-world experiments show that our proposed algorithm effectively protects data privacy.
doi_str_mv 10.3390/su142416842
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subjects Accuracy
Adaptive algorithms
Algorithms
Artificial intelligence
Computational linguistics
Data integrity
Datasets
Deep learning
Electronic banking
Fraud
Health care
Health services
Hospitals
Internet of medical things
Internet of Things
Language processing
Machine learning
Malware
Medical advice systems
Natural language interfaces
Optimization techniques
Patients
Performance management
Privacy
Remote monitoring
Safety and security measures
Security
Sustainability
Technology
title Gradient Boosting for Health IoT Federated Learning
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