  With the rising demand for wireless services and increased awareness of the
need for data protection, existing network traffic analysis and management
architectures are facing unprecedented challenges in classifying and
synthesizing the increasingly diverse services and applications. This paper
proposes FS-GAN, a federated self-supervised learning framework to support
automatic traffic analysis and synthesis over a large number of heterogeneous
datasets. FS-GAN is composed of multiple distributed Generative Adversarial
Networks (GANs), with a set of generators, each being designed to generate
synthesized data samples following the distribution of an individual service
traffic, and each discriminator being trained to differentiate the synthesized
data samples and the real data samples of a local dataset. A federated
learning-based framework is adopted to coordinate local model training
processes of different GANs across different datasets. FS-GAN can classify data
of unknown types of service and create synthetic samples that capture the
traffic distribution of the unknown types. We prove that FS-GAN can minimize
the Jensen-Shannon Divergence (JSD) between the distribution of real data
across all the datasets and that of the synthesized data samples. FS-GAN also
maximizes the JSD among the distributions of data samples created by different
generators, resulting in each generator producing synthetic data samples that
follow the same distribution as one particular service type. Extensive
simulation results show that the classification accuracy of FS-GAN achieves
over 20% improvement in average compared to the state-of-the-art
clustering-based traffic analysis algorithms. FS-GAN also has the capability to
synthesize highly complex mixtures of traffic types without requiring any
human-labeled data samples.
