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master's thesis
Federalno učenje nad tokovima podataka
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:242309
Namačinski, Luka
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Namačinski, L. (2022). Federalno učenje nad tokovima podataka (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:242309

Namačinski, Luka. "Federalno učenje nad tokovima podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:242309

Namačinski, Luka. "Federalno učenje nad tokovima podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:242309

Namačinski, L. (2022). 'Federalno učenje nad tokovima podataka', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 04 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:242309

Namačinski L. Federalno učenje nad tokovima podataka [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 November 04] Available at: https://urn.nsk.hr/urn:nbn:hr:168:242309

L. Namačinski, "Federalno učenje nad tokovima podataka", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:242309

Metadata
TitleFederalno učenje nad tokovima podataka
Title (english)Federated Learning Over Data Streams
AuthorLuka Namačinski
MentorIgor Čavrak (mentor)
Committee memberIgor Čavrak (član povjerenstva)
Committee memberIvana Bosnić (član povjerenstva)
Committee memberDanko Basch (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-01, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Rad daje pregled područja federalnog učenja. Predstavlja motivaciju i okolnosti koje su dovele do njegovog razvoja. Nadalje opisujemo ključne izazove s kojima se federalno učenje suočava. Također prikazuje korisnu taksonomiju za kategorizaciju i analizu sustava za federalno učenje. Predlaže sustav za federalno učenje implementiran u programskom jeziku Python i sustavu za razmjenjivanje poruka Apache Kafka. Objašnjava motivaciju odabira konkretnih tehnologija i način na koje su integrirane u sustav. Konačno, prikazujemo kratku analizu performansi izgrađenog sustava. Sustav je testiran za preciznost i skalabilnost sa zaključkom da postiže zadovoljavajuće performanse u obje kategorije.
Abstract (english)
This thesis presents an overview of federated learning. It shows the circumstances and motivation which led to its development as well as the challenges that federated learning faces. We present a categorization of federated learning, useful for analyzing a given federated learning system. Furthermore, we present an implemented federated learning system implemented in Python using the distributed messaging system Apache Kafka. Moreover, we present the reasoning behind the selection of these technologies and how they were incorporated into the system. Finally, we analyze the performance of the created system. Model quality and scalability were tested with the results showing that the system performed well
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:242309
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-14 09:35:07