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.

翻译：随着无线服务需求的增长和数据保护意识的增强，现有网络流量分析与管理架构在分类和合成日益多样化的服务与应用时，面临着前所未有的挑战。本文提出FS-GAN，一种联邦自监督学习框架，用于支持大量异构数据集上的自动流量分析与合成。FS-GAN由多个分布式生成对抗网络（GANs）组成，每组包含一套生成器，每个生成器设计用于生成遵循单个服务流量分布规律的合成数据样本，而每个判别器则被训练以区分本地数据集中的合成数据样本与真实数据样本。采用基于联邦学习的框架来协调不同数据集中不同GANs的本地模型训练过程。FS-GAN能够对未知服务类型的数据进行分类，并创建捕捉未知类型流量分布规律的合成样本。我们证明FS-GAN能够最小化所有数据集上真实数据分布与合成数据样本分布之间的Jensen-Shannon散度（JSD）。同时，FS-GAN还能最大化不同生成器所创建数据样本分布之间的JSD，从而使每个生成器生成的合成数据样本与某一特定服务类型的分布规律保持一致。大量仿真结果表明，与最先进的基于聚类的流量分析算法相比，FS-GAN的分类准确率平均提高了20%以上。此外，FS-GAN无需任何人工标注的数据样本，即可合成高度复杂的混合流量类型。