Primjena FedAvg algoritma u sustavima za udaljeno prikupljanje podataka

master's thesis

• Title (english): Application of the FedAvg Algorithm in Remote Data Collection Systems
• Author: Petar Jukica
• Mentor: Igor Čavrak (mentor)
• Committee member: Igor Čavrak (predsjednik povjerenstva)
• Committee member: Hrvoje Mlinarić (član povjerenstva)
• Committee member: Ivana Bosnić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2021-07-15, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Ovaj rad analizira potencijalne mogućnosti tehnike federativnog učenja, FedAvg, na uređajima ograničenih performansi. Pojašnjava se koncept federativnog učenja te se ukratko opisuju temeljne kategorije. Navode se mogući slučajevi uporabe federativnog učenja u razvoju stvarnih rješenja te problemi koje metoda rješava. Implementacija se odnosi na kategoriju horizontalnog federativnog učenja u kojoj je korišten ESP32 mikrokontroler za treniranje modela neuronske mreže nad skupom podataka MNIST. Razmatraju se rubni slučajevi te mogući problemi kod korištenja metode federativnog učenja. Rezultati eksperimenata su komentirani i navode se sljedeći koraci koji bi poboljšali postojeću implementaciju.

Abstract (english)

This thesis analyses the potential capabilities and the use cases of federated learning method called federated averaging on the devices of extremely limited memory size and processing power. Federated learning concept is explained and its basic categories are briefly summarized. Possible use cases of federated learning are mentioned, as are the issues that could be mitigated with the use of its methods. ESP32 microcontroller was used in order to perform the training of the neural network model on the MNIST dataset. Edge cases of the generated federated averaging model were studied and possible issues were raised. Results of the experiments have been commented and next increments in the implementation of training algorithm have been proposed.

• Keywords: Federativno učenje; FedAvg; ESP32; Strojno učenje; MNIST
• Keywords (english): Federative learning; Federated averaging; ESP32; Machine learning; MNIST
• Language: croatian
• URN:NBN: urn:nbn:hr:168:726201
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-13 15:11:41