An Architecture for a Blockchain-Enhanced Federated Learning

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dc.contributor.authorIwegbulam, C.M.
dc.contributor.authorOdumuyiwa, V.
dc.date.accessioned2024-11-04T10:28:29Z
dc.date.available2024-11-04T10:28:29Z
dc.date.issued2024-10-28
dc.descriptionScholarly article
dc.description.abstractThis research develops a decentralised machine learning technique for collaborative modelling that uses federated learning and blockchain technology to achieve model convergence. A blockchain-coordinated architecture is implemented enabling client nodes to train models on local private data and securely share model updates to build an aggregated global model. Extensive experiments validate the decentralized learning protocol on healthcare datasets. The federated models achieved convergence in root mean squared error metric at the sixth federated learning round by consolidating dispersed data. The results show that the secure multi-party computation method can be used to create accurate, combined models from data stored in different places and still achieve model convergence. However, there are overhead and scalability restrictions. This study lays the groundwork for decentralised machine learning with blockchain.
dc.identifier.citationIwegbulam C. M. & Odumuyiwa V. (2023). “An Architecture for a Blockchain-Enhanced Federated Learning". In Proceedings of the International Conference on Artificial Intelligence and Robotics (MIRG-ICAIR 2023), pp. 13-23, MIRG
dc.identifier.isbn978-978-767-697-4
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/13021
dc.language.isoen
dc.publisherMIRG
dc.relation.ispartofseriesMIRG-ICAIR 2023
dc.titleAn Architecture for a Blockchain-Enhanced Federated Learning
dc.typeArticle
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