A Federated Learning Approach to Traffic Matrix Estimation using Super-resolution Techniques
Abstract
Network measurement and telemetry techniques are central to the management of modern computer networks. Traffic matrices estimation is a popular technique that supports several applications. Existing approaches use statistical methods which often make invalid assumptions about the structure of the traffic matrix. Data-driven methods, instead, leverage detailed information about the network topology that may be unavailable or impractical to collect. In this work, we propose a super-resolution technique for traffic matrix estimation that can infer fine-grained network traffic. In our experiment, we demonstrate that the proposed approach with high precision outperforms existing data interpolation techniques. We also expand our design by employing a federated learning model to address scalability and improve performance. Such a model increases the accuracy of our inference with respect to its centralized counterpart, significantly lowering the number of training epochs.
Acknowledgement
This work is the result of a research project developed in collaboration with the Networking Research Group, led by Prof. Flavio Esposito, at the Saint Louis University of Saint Louis, in the United States.
Roberto Amoroso
ELLIS PhD Student, AI & Computer Vision
International Doctorate in ICT
My research interests include Open-vocabulary Image Segmentation and Multimodal Video Understanding.