A comprehensive comparison between federated and centralizedlearning

Abstract

Federated learning is an upcoming machine learning concept which allows data from multiple sources be usedfor training of classifiers without said data leaving its origin. In certain research cases using highly privatedata, the step of gathering data can be quite tedious. In such cases, federated learning has the potential tovastly speed up the research cycle. However, the question arises whether such a federated framework givessimilar performance compared to a central model with access to all data, in other words: Whether it mightbe worth the hassle of gathering all data anyway due to the performance difference. In this work, we providean extensive set of experiments comparing central and federated models, using multiple classifiers on multipledatasets. Results show that federated learning indeed has the potential to provide similar results, but thatits nature might enable use cases in which challenges with regards to batch effects between different datasetscould become prevalent