  Network traffic classification, a task to classify network traffic and
identify its type, is the most fundamental step to improve network services and
manage modern networks. Classical machine learning and deep learning method
have developed well in the field of network traffic classification. However,
there are still two major challenges. One is how to protect the privacy of
users' traffic data, and the other is that it is difficult to obtain labeled
data in reality. In this paper, we propose a novel approach using federated
semi-supervised learning for network traffic classification. In our approach,
the federated servers and several clients work together to train a global
classification model. Among them, unlabeled data is used on the client, and
labeled data is used on the server. Moreover, we use two traffic subflow
sampling methods: simple sampling and incremental sampling for data
preprocessing. The experimental results in the QUIC dataset show that the
accuracy of our federated semi-supervised approach can reach 91.08% and 97.81%
when using the simple sampling method and incremental sampling method
respectively. The experimental results also show that the accuracy gap between
our method and the centralized training method is minimal, and it can
effectively protect users' privacy and does not require a large amount of
labeled data.
