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Scientific paper - Original scientific paper
Framework for Federated Learning Open Models in e Government Applications
Interdisciplinary Description of Complex Systems : INDECS, 20 (2022), 2; 162-178. https://doi.org/10.7906/indecs.20.2.8
(TODO) Guberović, Emanuel; Alexopoulos, Charalampos; Bosnić, Ivana; Čavrak, Igor

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Guberović, E., Alexopoulos, C., Bosnić, I. & Čavrak, I.. Framework for Federated Learning Open Models in e Government Applications. Interdisciplinary Description of Complex Systems : INDECS, 20. (2), 162-178. doi: 10.7906/indecs.20.2.8

Guberović, Emanuel, et al. "Framework for Federated Learning Open Models in e Government Applications." Interdisciplinary Description of Complex Systems : INDECS, vol. 20, no. 2, pp. 162-178. https://doi.org/10.7906/indecs.20.2.8

Guberović, Emanuel, Charalampos Alexopoulos, Ivana Bosnić and Igor Čavrak. "Framework for Federated Learning Open Models in e Government Applications." Interdisciplinary Description of Complex Systems : INDECS 20, no. 2 162-178. https://doi.org/10.7906/indecs.20.2.8

Guberović, E., et al. 'Framework for Federated Learning Open Models in e Government Applications', Interdisciplinary Description of Complex Systems : INDECS, 20(2), pp. 162-178. doi: 10.7906/indecs.20.2.8

Guberović E, Alexopoulos C, Bosnić I, Čavrak I. Framework for Federated Learning Open Models in e Government Applications. Interdisciplinary Description of Complex Systems : INDECS [Internet]. [cited 2024 April 24];20(2):162-178. doi: 10.7906/indecs.20.2.8

E. Guberović, C. Alexopoulos, I. Bosnić and I. Čavrak, "Framework for Federated Learning Open Models in e Government Applications", Interdisciplinary Description of Complex Systems : INDECS, vol. 20, no. 2, pp. 162-178. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:168:069522. [Accessed: 24 April 2024]

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Metadata
Title (english)Framework for Federated Learning Open Models in e Government Applications
AuthorEmanuel Guberović
AuthorCharalampos Alexopoulos
AuthorIvana Bosnić
AuthorIgor Čavrak
CollaborationTODO
Author's institutionUniversity of Zagreb
Faculty of Electrical Engineering and Computing
(Department of Control and Computer Engineering)
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract (english)
Using open data and artificial intelligence in providing innovative public services is the focus of the third generation of e-Government and supporting Internet and Communication Technologies systems. However, developing applications and offering open services based on (open) machine learning models requires large volumes of private, open, or a combination of both open and private data for model training to achieve sufficient model quality. Therefore, it would be beneficial to use both open and private data simultaneously to fully use the potential that machine learning could grant to the public and private sectors. Federated learning, as a machine learning technique, enables collaborative learning among different parties and their data, being private or open, creating shared knowledge by training models on such partitioned data without sharing it between parties in any step of the training or inference process. This paper provides a practical layout for developing and sharing machine learning models in a federative and open manner called Federated Learning Open Model. The definition of the Federated Learning Open Model concept is followed by a description of two potential use cases and services achieved with its usage, one being from the agricultural sector with the horizontal dataset partitioning and the latter being from the financial sector with a dataset partitioned vertically.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleInterdisciplinary Description of Complex Systems : INDECS
Numberingvol. 20, no. 2, pp. 162-178
BPC0 HRK
p-ISSN1334-4684
e-ISSN1334-4676
DOIhttps://doi.org/10.7906/indecs.20.2.8
URN:NBNurn:nbn:hr:168:069522
Accepted2022-03-25
Document URLhttp://indecs.eu/index.php?s=x&y=2022&p=162-178
Type of resourceText
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2023-05-09 18:55:37