Federated learning: A comparison of methods

How do different ML models compare to each other

Bachelor thesis (2023)

Authors

E. Sīpols Electrical Engineering, Mathematics and Computer Science

Contributors

S.J.F. Garst Pattern Recognition and Bioinformatics - EEMCS (mentor)
M.J.T. Reinders Pattern Recognition and Bioinformatics - EEMCS (graduation committee member)
Lydia Y. Chen Data-Intensive Systems - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Emīls Sīpols
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Published Date

28-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Federated learning (FL) has emerged as a promis-ing approach for training machine learning models using geographically distributed data. This paper presents a comprehensive comparative study of var-ious machine learning models in the context of FL. The aim is to evaluate the efficacy of these models in different data distribution scenarios and provide
practical insights for practitioners in the field. The findings highlight the performance and limitations of linear and non-linear models on MNIST and Ki-nase datasets.