@INPROCEEDINGS{Amor2301:Federated,
AUTHOR="Roberto Amoroso and Lorenzo Pappone and Flavio Esposito",
TITLE="A Federated Learning Approach to Traffic Matrix Estimation using
Super-resolution Techniques",
BOOKTITLE="2023 IEEE 20th Consumer Communications \& Networking Conference (CCNC)
(CCNC 2023)",
ADDRESS="Las Vegas, USA",
MONTH=jan,
YEAR=2023,
KEYWORDS="super resolution; computer vision; traffic matrix; network measurement;
deep learning; federated learning",
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."
}