A reliable and privacy-preserved federated learning framework for real-time smoking prediction in healthcare
The ever-evolving domain of machine learning has witnessed significant advancements with the advent of federated learning, a paradigm revered for its capacity to facilitate model training on decentralized data sources while upholding data confidentiality. This research introduces a federated learnin...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Computer Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcomp.2024.1494174/full |
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| author | Siddhesh Fuladi D. Ruby N. Manikandan Animesh Verma M. K. Nallakaruppan Shitharth Selvarajan Shitharth Selvarajan Shitharth Selvarajan Preeti Meena V. P. Meena Ibrahim A. Hameed |
| author_facet | Siddhesh Fuladi D. Ruby N. Manikandan Animesh Verma M. K. Nallakaruppan Shitharth Selvarajan Shitharth Selvarajan Shitharth Selvarajan Preeti Meena V. P. Meena Ibrahim A. Hameed |
| author_sort | Siddhesh Fuladi |
| collection | DOAJ |
| description | The ever-evolving domain of machine learning has witnessed significant advancements with the advent of federated learning, a paradigm revered for its capacity to facilitate model training on decentralized data sources while upholding data confidentiality. This research introduces a federated learning-based framework designed to address gaps in existing smoking prediction models, which often compromise privacy and lack data generalizability. By utilizing a distributed approach, the framework ensures secure, privacy-preserved model training on decentralized devices, enabling the capture of diverse smoking behavior patterns. The proposed framework incorporates careful data preprocessing, rational model architecture selection, and optimal parameter tuning to predict smoking with high precision. The results demonstrate the efficacy of the model, achieving an accuracy rate of 97.65%, complemented by an F1-score of 97.41%, precision of 97.31%, and recall rate of 97.36%, significantly outperforming traditional approaches. This research also discusses the benefits of federated learning, including efficient time management, parallel processing, secure model updates, and enhanced data privacy, while addressing limitations such as computational overhead. These findings underscore the transformative potential of federated learning in healthcare, paving the way for future advancements in privacy-preserved predictive modeling. |
| format | Article |
| id | doaj-art-1acbee4ad83e46ed8032726d3481559d |
| institution | Kabale University |
| issn | 2624-9898 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computer Science |
| spelling | doaj-art-1acbee4ad83e46ed8032726d3481559d2025-01-22T07:15:14ZengFrontiers Media S.A.Frontiers in Computer Science2624-98982025-01-01610.3389/fcomp.2024.14941741494174A reliable and privacy-preserved federated learning framework for real-time smoking prediction in healthcareSiddhesh Fuladi0D. Ruby1N. Manikandan2Animesh Verma3M. K. Nallakaruppan4Shitharth Selvarajan5Shitharth Selvarajan6Shitharth Selvarajan7Preeti Meena8V. P. Meena9Ibrahim A. Hameed10School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaBalaji Institute of Modern Management, Sri Balaji University, Pune, IndiaSchool of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, United KingdomDepartment of Computer Science and Engineering, Chennai Institute of Technology, Chennai, IndiaCentre for Research Impact & Outcome, Chitkara University, Rajpura, PunjabDepartment of Electrical Engineering, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan, IndiaDepartment of Electrical Engineering, National Institute of Technology Jamshedpur, Jamshedpur, Jharkhand, IndiaDepartment of ICT and Natural Sciences, Norwegian University of Science and Technology, Trondheim, NorwayThe ever-evolving domain of machine learning has witnessed significant advancements with the advent of federated learning, a paradigm revered for its capacity to facilitate model training on decentralized data sources while upholding data confidentiality. This research introduces a federated learning-based framework designed to address gaps in existing smoking prediction models, which often compromise privacy and lack data generalizability. By utilizing a distributed approach, the framework ensures secure, privacy-preserved model training on decentralized devices, enabling the capture of diverse smoking behavior patterns. The proposed framework incorporates careful data preprocessing, rational model architecture selection, and optimal parameter tuning to predict smoking with high precision. The results demonstrate the efficacy of the model, achieving an accuracy rate of 97.65%, complemented by an F1-score of 97.41%, precision of 97.31%, and recall rate of 97.36%, significantly outperforming traditional approaches. This research also discusses the benefits of federated learning, including efficient time management, parallel processing, secure model updates, and enhanced data privacy, while addressing limitations such as computational overhead. These findings underscore the transformative potential of federated learning in healthcare, paving the way for future advancements in privacy-preserved predictive modeling.https://www.frontiersin.org/articles/10.3389/fcomp.2024.1494174/fullfederated learningmachine learningprivacy preservationdecentralized dataenhanced data securitydata preprocessing |
| spellingShingle | Siddhesh Fuladi D. Ruby N. Manikandan Animesh Verma M. K. Nallakaruppan Shitharth Selvarajan Shitharth Selvarajan Shitharth Selvarajan Preeti Meena V. P. Meena Ibrahim A. Hameed A reliable and privacy-preserved federated learning framework for real-time smoking prediction in healthcare Frontiers in Computer Science federated learning machine learning privacy preservation decentralized data enhanced data security data preprocessing |
| title | A reliable and privacy-preserved federated learning framework for real-time smoking prediction in healthcare |
| title_full | A reliable and privacy-preserved federated learning framework for real-time smoking prediction in healthcare |
| title_fullStr | A reliable and privacy-preserved federated learning framework for real-time smoking prediction in healthcare |
| title_full_unstemmed | A reliable and privacy-preserved federated learning framework for real-time smoking prediction in healthcare |
| title_short | A reliable and privacy-preserved federated learning framework for real-time smoking prediction in healthcare |
| title_sort | reliable and privacy preserved federated learning framework for real time smoking prediction in healthcare |
| topic | federated learning machine learning privacy preservation decentralized data enhanced data security data preprocessing |
| url | https://www.frontiersin.org/articles/10.3389/fcomp.2024.1494174/full |
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