| Autor: |
Haitham Afifi, Sabrina Pochaba, Andreas Boltres, Dominic Laniewski, Janek Haberer, Leonard Paeleke, Reza Poorzare, Daniel Stolpmann, Nikolas Wehner, Adrian Redder, Eric Samikwa, Michael Seufert |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 12, Pp 54673-54720 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3384460 |
| Popis: |
Machine learning has found many applications in network contexts. These include solving optimisation problems and managing network operations. Conversely, networks are essential for facilitating machine learning training and inference, whether performed centrally or in a distributed fashion. To conduct rigorous research in this area, researchers must have a comprehensive understanding of fundamental techniques, specific frameworks, and access to relevant datasets. Additionally, access to training data can serve as a benchmark or a springboard for further investigation. All these techniques are summarized in this article; serving as a primer paper and hopefully providing an efficient start for anybody doing research regarding machine learning for networks or using networks for machine learning. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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