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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

Institucijski repozitorij: Repozitorij FER-a

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Guberović, E., Alexopoulos, C., Bosnić, I. 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, br. 2, str. 162-178. https://doi.org/10.7906/indecs.20.2.8

Guberović, Emanuel, Charalampos Alexopoulos, Ivana Bosnić i Igor Čavrak. "Framework for Federated Learning Open Models in e Government Applications." Interdisciplinary Description of Complex Systems : INDECS 20, br. 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), str. 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]. [pristupljeno 26.12.2024.];20(2):162-178. doi: 10.7906/indecs.20.2.8

E. Guberović, C. Alexopoulos, I. Bosnić i I. Čavrak, "Framework for Federated Learning Open Models in e Government Applications", Interdisciplinary Description of Complex Systems : INDECS, vol. 20, br. 2, str. 162-178. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:168:069522. [Citirano: 26.12.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:168:069522
Podaci o radu
Naslov (engleski)Framework for Federated Learning Open Models in e Government Applications
AutorEmanuel Guberović
AutorCharalampos Alexopoulos
AutorIvana Bosnić
AutorIgor Čavrak
KolaboracijaTODO
Autorova ustanovaSveučilište u Zagrebu
Fakultet elektrotehnike i računarstva
(Zavod za automatiku i računalno inženjerstvo)
Znanstveno / umjetničko
područje, polje i grana		TEHNIČKE ZNANOSTI
Računarstvo
Sažetak (engleski)
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.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaInterdisciplinary Description of Complex Systems : INDECS
Brojčani podacivol. 20, br. 2, str. 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
Datum prihvaćanja za tisak2022-03-25
URL dokumentahttp://indecs.eu/index.php?s=x&y=2022&p=162-178
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Zaštićeno autorskim pravom.
Datum i vrijeme pohrane2023-05-09 18:55:37