ES
E. Sīpols
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
1 records found
1
Federated learning: A comparison of methods
How do different ML models compare to each other
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. ...
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. ...
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.
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.