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...
Gespeichert in:
Veröffentlicht in: | Sustainability 2022-12, Vol.14 (24), p.16842 |
---|---|
Hauptverfasser: | , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
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 |
format | Article |
fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_2756821325</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A746698171</galeid><sourcerecordid>A746698171</sourcerecordid><originalsourceid>FETCH-LOGICAL-c371t-d561c71d5becaa0696d343a3d0560179763a5b5c84c8bfb6c010b75a5d9389b43</originalsourceid><addsrcrecordid>eNpVkE9LAzEQxYMoWGpPfoEFTyJbM5tNsjnWYv9AQdB6DtkkW7e0m5pkQb99I_XQzhxmGH7vDTyE7gGPCRH4OfRQFiWwqiyu0KDAHHLAFF-f7bdoFMIWpyIEBLABInOvTGu7mL04F2LbbbLG-Wxh1S5-ZUu3zmbWWK-iNdnKKt8l4g7dNGoX7Oh_DtHn7HU9XeSrt_lyOlnlmnCIuaEMNAdDa6uVwkwwQ0qiiMGUYeCCM6JoTXVV6qpuaqYx4JpTRY0glahLMkQPJ9-Dd9-9DVFuXe-79FIWnLKqAFLQRI1P1EbtrGy7xkWvdGpj9612nW3adJ_wkjFRAYckeLwQJCban7hRfQhy-fF-yT6dWO1dCN428uDbvfK_ErD8S12epU6OnZNwpw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2756821325</pqid></control><display><type>article</type><title>Gradient Boosting for Health IoT Federated Learning</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Wassan, Sobia ; Suhail, Beenish ; Mubeen, Riaqa ; Raj, Bhavana ; Agarwal, Ujjwal ; Khatri, Eti ; Gopinathan, Sujith ; Dhiman, Gaurav</creator><creatorcontrib>Wassan, Sobia ; Suhail, Beenish ; Mubeen, Riaqa ; Raj, Bhavana ; Agarwal, Ujjwal ; Khatri, Eti ; Gopinathan, Sujith ; Dhiman, Gaurav</creatorcontrib><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.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su142416842</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>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</subject><ispartof>Sustainability, 2022-12, Vol.14 (24), p.16842</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c371t-d561c71d5becaa0696d343a3d0560179763a5b5c84c8bfb6c010b75a5d9389b43</citedby><cites>FETCH-LOGICAL-c371t-d561c71d5becaa0696d343a3d0560179763a5b5c84c8bfb6c010b75a5d9389b43</cites><orcidid>0000-0002-8150-3018 ; 0000-0001-5504-7496 ; 0000-0002-6343-5197 ; 0000-0002-6290-0149 ; 0000-0002-3206-8865</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Wassan, Sobia</creatorcontrib><creatorcontrib>Suhail, Beenish</creatorcontrib><creatorcontrib>Mubeen, Riaqa</creatorcontrib><creatorcontrib>Raj, Bhavana</creatorcontrib><creatorcontrib>Agarwal, Ujjwal</creatorcontrib><creatorcontrib>Khatri, Eti</creatorcontrib><creatorcontrib>Gopinathan, Sujith</creatorcontrib><creatorcontrib>Dhiman, Gaurav</creatorcontrib><title>Gradient Boosting for Health IoT Federated Learning</title><title>Sustainability</title><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.</description><subject>Accuracy</subject><subject>Adaptive algorithms</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Computational linguistics</subject><subject>Data integrity</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Electronic banking</subject><subject>Fraud</subject><subject>Health care</subject><subject>Health services</subject><subject>Hospitals</subject><subject>Internet of medical things</subject><subject>Internet of Things</subject><subject>Language processing</subject><subject>Machine learning</subject><subject>Malware</subject><subject>Medical advice systems</subject><subject>Natural language interfaces</subject><subject>Optimization techniques</subject><subject>Patients</subject><subject>Performance management</subject><subject>Privacy</subject><subject>Remote monitoring</subject><subject>Safety and security measures</subject><subject>Security</subject><subject>Sustainability</subject><subject>Technology</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpVkE9LAzEQxYMoWGpPfoEFTyJbM5tNsjnWYv9AQdB6DtkkW7e0m5pkQb99I_XQzhxmGH7vDTyE7gGPCRH4OfRQFiWwqiyu0KDAHHLAFF-f7bdoFMIWpyIEBLABInOvTGu7mL04F2LbbbLG-Wxh1S5-ZUu3zmbWWK-iNdnKKt8l4g7dNGoX7Oh_DtHn7HU9XeSrt_lyOlnlmnCIuaEMNAdDa6uVwkwwQ0qiiMGUYeCCM6JoTXVV6qpuaqYx4JpTRY0glahLMkQPJ9-Dd9-9DVFuXe-79FIWnLKqAFLQRI1P1EbtrGy7xkWvdGpj9612nW3adJ_wkjFRAYckeLwQJCban7hRfQhy-fF-yT6dWO1dCN428uDbvfK_ErD8S12epU6OnZNwpw</recordid><startdate>20221201</startdate><enddate>20221201</enddate><creator>Wassan, Sobia</creator><creator>Suhail, Beenish</creator><creator>Mubeen, Riaqa</creator><creator>Raj, Bhavana</creator><creator>Agarwal, Ujjwal</creator><creator>Khatri, Eti</creator><creator>Gopinathan, Sujith</creator><creator>Dhiman, Gaurav</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-8150-3018</orcidid><orcidid>https://orcid.org/0000-0001-5504-7496</orcidid><orcidid>https://orcid.org/0000-0002-6343-5197</orcidid><orcidid>https://orcid.org/0000-0002-6290-0149</orcidid><orcidid>https://orcid.org/0000-0002-3206-8865</orcidid></search><sort><creationdate>20221201</creationdate><title>Gradient Boosting for Health IoT Federated Learning</title><author>Wassan, Sobia ; Suhail, Beenish ; Mubeen, Riaqa ; Raj, Bhavana ; Agarwal, Ujjwal ; Khatri, Eti ; Gopinathan, Sujith ; Dhiman, Gaurav</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c371t-d561c71d5becaa0696d343a3d0560179763a5b5c84c8bfb6c010b75a5d9389b43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Adaptive algorithms</topic><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Computational linguistics</topic><topic>Data integrity</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>Electronic banking</topic><topic>Fraud</topic><topic>Health care</topic><topic>Health services</topic><topic>Hospitals</topic><topic>Internet of medical things</topic><topic>Internet of Things</topic><topic>Language processing</topic><topic>Machine learning</topic><topic>Malware</topic><topic>Medical advice systems</topic><topic>Natural language interfaces</topic><topic>Optimization techniques</topic><topic>Patients</topic><topic>Performance management</topic><topic>Privacy</topic><topic>Remote monitoring</topic><topic>Safety and security measures</topic><topic>Security</topic><topic>Sustainability</topic><topic>Technology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wassan, Sobia</creatorcontrib><creatorcontrib>Suhail, Beenish</creatorcontrib><creatorcontrib>Mubeen, Riaqa</creatorcontrib><creatorcontrib>Raj, Bhavana</creatorcontrib><creatorcontrib>Agarwal, Ujjwal</creatorcontrib><creatorcontrib>Khatri, Eti</creatorcontrib><creatorcontrib>Gopinathan, Sujith</creatorcontrib><creatorcontrib>Dhiman, Gaurav</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wassan, Sobia</au><au>Suhail, Beenish</au><au>Mubeen, Riaqa</au><au>Raj, Bhavana</au><au>Agarwal, Ujjwal</au><au>Khatri, Eti</au><au>Gopinathan, Sujith</au><au>Dhiman, Gaurav</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Gradient Boosting for Health IoT Federated Learning</atitle><jtitle>Sustainability</jtitle><date>2022-12-01</date><risdate>2022</risdate><volume>14</volume><issue>24</issue><spage>16842</spage><pages>16842-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>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.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/su142416842</doi><orcidid>https://orcid.org/0000-0002-8150-3018</orcidid><orcidid>https://orcid.org/0000-0001-5504-7496</orcidid><orcidid>https://orcid.org/0000-0002-6343-5197</orcidid><orcidid>https://orcid.org/0000-0002-6290-0149</orcidid><orcidid>https://orcid.org/0000-0002-3206-8865</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2071-1050 |
ispartof | Sustainability, 2022-12, Vol.14 (24), p.16842 |
issn | 2071-1050 2071-1050 |
language | eng |
recordid | cdi_proquest_journals_2756821325 |
source | MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals |
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 |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-19T07%3A15%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Gradient%20Boosting%20for%20Health%20IoT%20Federated%20Learning&rft.jtitle=Sustainability&rft.au=Wassan,%20Sobia&rft.date=2022-12-01&rft.volume=14&rft.issue=24&rft.spage=16842&rft.pages=16842-&rft.issn=2071-1050&rft.eissn=2071-1050&rft_id=info:doi/10.3390/su142416842&rft_dat=%3Cgale_proqu%3EA746698171%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2756821325&rft_id=info:pmid/&rft_galeid=A746698171&rfr_iscdi=true |