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master's thesis
Federalno učenje u ugradbenim sustavima
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:929288
Premužić, Ivan
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Premužić, I. (2021). Federalno učenje u ugradbenim sustavima (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:929288

Premužić, Ivan. "Federalno učenje u ugradbenim sustavima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:929288

Premužić, Ivan. "Federalno učenje u ugradbenim sustavima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:929288

Premužić, I. (2021). 'Federalno učenje u ugradbenim sustavima', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 04 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:929288

Premužić I. Federalno učenje u ugradbenim sustavima [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 November 04] Available at: https://urn.nsk.hr/urn:nbn:hr:168:929288

I. Premužić, "Federalno učenje u ugradbenim sustavima", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:929288

Metadata
TitleFederalno učenje u ugradbenim sustavima
Title (english)Federated Learning in Embedded Systems
AuthorIvan Premužić
MentorIgor Čavrak (mentor)
Committee memberIgor Čavrak (predsjednik povjerenstva)
Committee memberHrvoje Mlinarić (član povjerenstva)
Committee memberIvana Bosnić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-15, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Federalnog učenje je kolaborativna, distribuirana metoda za učenje globalnog dijeljenog modela. Koristi računalne mogućnosti krajnjih uređaja (pametni telefoni, uređaji u ugradbenim sustavima, internetu stvari, računarstvu na rubu itd.) za obavljanje procesa strojnog učenja, pri čemu se za treniranje koriste lokalni podaci. Svaki krajnji uređaj (klijentski čvor) trenira lokalni model nad svojim podacima, a centralni čvor kreira globalni model na temelju lokalnih modela. Lokalni podaci svakog klijenta, koji su često privatni, nikada se ne dijele s nekom trećom stranom i na taj način je sačuvana privatnost klijenta. Kako bi se olakšalo istraživanje federalnog učenja razvijeni su programski okviri za federalno učenje, a jedan od njih je Flower. U ovom radu proučeno je područje federalnog učenja, te programski okvir Flower i njegove karakteristike i prednosti u odnosu na slične programske okvire. Implementiran je sustav za stvaranje zajedničkog modela temeljen na programskom okviru Flower, te su ispitane performanse sustava s obzirom na dostignuti kvalitetu zajedničkog globalnog modela i modela dobivenog metodom klasičnog centralnog strojnog učenja.
Abstract (english)
Federated learning (FL) is collaborative, distributed method of machine learning used to create a shared global model. It uses computing capabilities of end devices (smartphones, devices in embedded systems, IoT, edge computing etc.) for machine learning on their local data. Every end device (client node) trains a local model on its local data, while central node creates global shared model based on information from local models. Local data of each client, which is often private, is never shared with other party and privacy of clients is preserved. To make exploration and development of federated learning easier, several frameworks for federated learning have been developed, one of which is Flower. In this work the field of federated learning is described and Flower framework for federated learning is explored. Using Flower framework, system for creating a shared global model is developed and its performance is evaluated based on achieved quality of shared global model and model created using classic centralized method of machine learning.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:929288
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 on2023-01-13 13:33:45