FL-MEC: federated learning for network traffic classification on the network Edge

• Autorzy: Paszko Patryk , Konieczny Marek , Zieliński Sławomir , Kwolek Bartosz

Identyfikatory

DOI

10.7494/csci.2025.26.4.7196

• Języki publikacji: EN

Abstrakty

EN

Nowadays, two technological trends, Federated Learning (FL) and Edge Computing (EC), are increasingly important and influential. FL is a decentralized machine learning strategy that allows learning on distributed data. It primarily allows performing learning operations close to the user, where the data is gathered. This approach belongs to 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 allour experiments on a virtualized environment that emulates existing mid-scale EC network infrastructure, including limitations related to resource constraints on edge nodes.

Słowa kluczowe

EN

network traffic classification; edge computing; federated learning

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2025
• Tom: T. 26 (4)
• Strony: 181--201
• Opis fizyczny: Bibliogr. 43 poz., rys., tab., wykr.

Twórcy

autor

Paszko Patryk

• ScData, Krakow, Poland

autor

Konieczny Marek

• AGH University of Krakow, Faculty of Computer Science, Krakow, Poland

autor

Zieliński Sławomir

• AGH University of Krakow, Faculty of Computer Science, Krakow, Poland

autor

Kwolek Bartosz

• AGH University of Krakow, Faculty of Computer Science, Krakow, Poland

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2026).