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Orbital learning: a novel, actively orchestrated decentralised learning for healthcare
Scientific Reports, Volume: 14, Issue: 1
Swansea University Authors:
Neeraj Kavan Chakshu, Perumal Nithiarasu
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© The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License.
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DOI (Published version): 10.1038/s41598-024-60915-9
Abstract
A novel collaborative and continual learning across a network of decentralised healthcare units, avoiding identifiable data-sharing capacity, is proposed. Currently available methodologies, such as federated learning and swarm learning, have demonstrated decentralised learning. However, the majority...
| Published in: | Scientific Reports |
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| ISSN: | 2045-2322 |
| Published: |
Springer Science and Business Media LLC
2024
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa66208 |
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| Abstract: |
A novel collaborative and continual learning across a network of decentralised healthcare units, avoiding identifiable data-sharing capacity, is proposed. Currently available methodologies, such as federated learning and swarm learning, have demonstrated decentralised learning. However, the majority of them face shortcomings that affect their performance and accuracy. These shortcomings include a non-uniform rate of data accumulation, non-uniform patient demographics, biased human labelling, and erroneous or malicious training data. A novel method to reduce such shortcomings is proposed in the present work through selective grouping and displacing of actors in a network of many entities for intra-group sharing of learning with inter-group accessibility. The proposed system, known as Orbital Learning, incorporates various features from split learning and ensemble learning for a robust and secure performance of supervised models. A digital embodiment of the information quality and flow within a decentralised network, this platform also acts as a digital twin of healthcare network. An example of ECG classification for arrhythmia with 6 clients is used to analyse its performance and is compared against federated learning. In this example, four separate experiments are conducted with varied configurations, such as varied age demographics and clients with data tampering. The results obtained show an average area under receiver operating characteristic curve (AUROC) of 0.819 (95% CI 0.784–0.853) for orbital learning whereas 0.714 (95% CI 0.692–0.736) for federated learning. This result shows an increase in overall performance and establishes that the proposed system can address the majority of the issues faced by existing decentralised learning methodologies. Further, a scalability demo conducted establishes the versatility and scalability of this platform in handling state-of-the-art large language models. |
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| Keywords: |
Decentralised learning; Digital health; Data security; Data privacy |
| College: |
Faculty of Science and Engineering |
| Funders: |
The first author would like to thank IMPACT at Swansea University for funding and supporting this project during their fellowship. Authors would also like to acknowledge Faculty of Science and Engineering, Swansea University for their support. This work was partially supported through Impact Acceleration Account grant number: EP/X525637/1. |
| Issue: |
1 |