DAG-Based Swarm Learning Approach in Healthcare: A Survey
Healthcare systems are advancing at a rapid pace as a result of new technologies to address several issues in the sector such as shortage of skilled health workers and to deal with new diseases like COVID-19. Incorporating technologies like blockchain, federated learning, swarm learning, and Directe...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10844296/ |
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| author | David Gana Faisal Jamil |
| author_facet | David Gana Faisal Jamil |
| author_sort | David Gana |
| collection | DOAJ |
| description | Healthcare systems are advancing at a rapid pace as a result of new technologies to address several issues in the sector such as shortage of skilled health workers and to deal with new diseases like COVID-19. Incorporating technologies like blockchain, federated learning, swarm learning, and Directed Acyclic Graphs is transforming healthcare. This article thoroughly examines recent progress and uses at the intersection of these technologies within the healthcare field. Blockchain’s innovative consensus mechanisms and secure data flow systems offer encouraging solutions to crucial issues in healthcare data management and security. Also, federated learning has been deployed in various ways to tackle healthcare challenges enabling collaborative data analysis while upholding patient confidentiality. Swarm learning algorithms have been notably effective in healthcare, enriching medical diagnostics, disease prognosis, and precision medicine. Solutions based on Directed Acyclic Graphs present scalable and effective alternative to traditional blockchain frameworks, providing improved consensus speed and decreased bottlenecks in transaction processing. These advancements signify a shift in direction towards fully decentralised and secure healthcare systems. This paper highlights the transformative impact of these technologies on medical diagnostics, disease prediction, and precision medicine. |
| format | Article |
| id | doaj-art-7c17114d110e4c309a800ad451747b48 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7c17114d110e4c309a800ad451747b482025-01-25T00:02:16ZengIEEEIEEE Access2169-35362025-01-0113137961381510.1109/ACCESS.2025.353121610844296DAG-Based Swarm Learning Approach in Healthcare: A SurveyDavid Gana0https://orcid.org/0009-0008-9414-0667Faisal Jamil1Department of Computer Science, University of Huddersfield, Huddersfield, U.K.Department of Computer Science, University of Huddersfield, Huddersfield, U.K.Healthcare systems are advancing at a rapid pace as a result of new technologies to address several issues in the sector such as shortage of skilled health workers and to deal with new diseases like COVID-19. Incorporating technologies like blockchain, federated learning, swarm learning, and Directed Acyclic Graphs is transforming healthcare. This article thoroughly examines recent progress and uses at the intersection of these technologies within the healthcare field. Blockchain’s innovative consensus mechanisms and secure data flow systems offer encouraging solutions to crucial issues in healthcare data management and security. Also, federated learning has been deployed in various ways to tackle healthcare challenges enabling collaborative data analysis while upholding patient confidentiality. Swarm learning algorithms have been notably effective in healthcare, enriching medical diagnostics, disease prognosis, and precision medicine. Solutions based on Directed Acyclic Graphs present scalable and effective alternative to traditional blockchain frameworks, providing improved consensus speed and decreased bottlenecks in transaction processing. These advancements signify a shift in direction towards fully decentralised and secure healthcare systems. This paper highlights the transformative impact of these technologies on medical diagnostics, disease prediction, and precision medicine.https://ieeexplore.ieee.org/document/10844296/Blockchaindirected acyclic graphfederated learningIoTprivacyscalability |
| spellingShingle | David Gana Faisal Jamil DAG-Based Swarm Learning Approach in Healthcare: A Survey IEEE Access Blockchain directed acyclic graph federated learning IoT privacy scalability |
| title | DAG-Based Swarm Learning Approach in Healthcare: A Survey |
| title_full | DAG-Based Swarm Learning Approach in Healthcare: A Survey |
| title_fullStr | DAG-Based Swarm Learning Approach in Healthcare: A Survey |
| title_full_unstemmed | DAG-Based Swarm Learning Approach in Healthcare: A Survey |
| title_short | DAG-Based Swarm Learning Approach in Healthcare: A Survey |
| title_sort | dag based swarm learning approach in healthcare a survey |
| topic | Blockchain directed acyclic graph federated learning IoT privacy scalability |
| url | https://ieeexplore.ieee.org/document/10844296/ |
| work_keys_str_mv | AT davidgana dagbasedswarmlearningapproachinhealthcareasurvey AT faisaljamil dagbasedswarmlearningapproachinhealthcareasurvey |