Federated learning: A comparison of methods
How do different ML models compare to each other
E. Sīpols (TU Delft - Electrical Engineering, Mathematics and Computer Science)
S.J.F. Garst (TU Delft - Pattern Recognition and Bioinformatics)
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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.