FL-MEC: FEDERATED LEARNING FOR NETWORK TRAFFIC CLASSIFICATION ON THE NETWORK EDGE

Patryk Paszko; Marek Konieczny; Sławomir  Zieliński; Bartosz Kwolek

doi:10.7494/csci.2025.26.4.7196

Authors

Patryk Paszko Patryk Paszko ScData, Krakow, Poland
Marek Konieczny AGH University of Krakow
Sławomir Zieliński AGH University of Krakow https://orcid.org/0000-0002-0824-2608
Bartosz Kwolek AGH University of Krakow

DOI:

https://doi.org/10.7494/csci.2025.26.4.7196

Abstract

Nowadays, two technological trends, Federated Learning (FL) and Edge Computing (EC), are becoming more and more important and influential. FL is a distributed machine learning strategy that allows learning on distributed data. It primarily allows performing learning operations close to the user, where we can gather data. This approach lies in the EC domain, where the main goal is to move computation closer to the end user (e.g., from the centralized cloud). In our work, we apply the FL and EC in the context of network flow classification. We achieved an accuracy of 0.957 with the FL model, compared to 0.924 for the best local model. We achieved these results thanks to the federated averaging performed on neural network layers. To verify our approach, we executed all our experiments on a virtualized environment that emulates existing mid-scale EC network infrastructure, including limitations related to resource constraints on edge nodes.

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