# Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks

arXiv: https://arxiv.org/abs/2205.00550 · HTML: https://mlsanity.com/paper/2205.00550/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 1 May 2022.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Federated Feature Selection for Cyber-Physical Systems of Systems](https://mlsanity.com/paper/2109.11323/) (2021)
- [M2M Traffic via Random Access Satellite links: Interactions between Transport and MAC Layers](https://mlsanity.com/paper/1609.03387/) (2016)

## Built on by (most cited first, 1 of 1)

- [A Federated Channel Modeling System using Generative Neural Networks](https://mlsanity.com/paper/2305.18856/) (2023)

## Abstract (arXiv)

Automatic traffic classification is increasingly becoming important in traffic engineering, as the current trend of encrypting transport information (e.g., behind HTTP-encrypted tunnels) prevents intermediate nodes from accessing end-to-end packet headers. However, this information is crucial for traffic shaping, network slicing, and Quality of Service (QoS) management, for preventing network intrusion, and for anomaly detection. 3D networks offer multiple routes that can guarantee different levels of QoS. Therefore, service classification and separation are essential to guarantee the required QoS level to each traffic sub-flow through the appropriate network trunk. In this paper, a federated feature selection and feature reduction learning scheme is proposed to classify network traffic in a semi-supervised cooperative manner. The federated gateways of 3D network help to enhance the global knowledge of network traffic to improve the accuracy of anomaly and intrusion detection and service identification of a new traffic flow.