  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.
