  With the fast growing demand on new services and applications as well as the
increasing awareness of data protection, traditional centralized traffic
classification approaches are facing unprecedented challenges. This paper
introduces a novel framework, Federated Generative Adversarial Networks and
Automatic Classification (FGAN-AC), which integrates decentralized data
synthesizing with traffic classification. FGAN-AC is able to synthesize and
classify multiple types of service data traffic from decentralized local
datasets without requiring a large volume of manually labeled dataset or
causing any data leakage. Two types of data synthesizing approaches have been
proposed and compared: computation-efficient FGAN
(FGAN-\uppercase\expandafter{\romannumeral1}) and communication-efficient FGAN
(FGAN-\uppercase\expandafter{\romannumeral2}). The former only implements a
single CNN model for processing each local dataset and the later only requires
coordination of intermediate model training parameters. An automatic data
classification and model updating framework has been proposed to automatically
identify unknown traffic from the synthesized data samples and create new
pseudo-labels for model training. Numerical results show that our proposed
framework has the ability to synthesize highly mixed service data traffic and
can significantly improve the traffic classification performance compared to
existing solutions.
