The increasing concerns of knowledge transfer and data privacy challenge the
traditional gather-and-analyse paradigm in networks. Specifically, the
intelligent orchestration of Virtual Network Functions (VNFs) requires
understanding and profiling the resource consumption. However, profiling all
kinds of VNFs is time-consuming. It is important to consider transferring the
well-profiled VNF knowledge to other lack-profiled VNF types while keeping data
private. To this end, this paper proposes a Federated Transfer Component
Analysis (FTCA) method between the source and target VNFs. FTCA first trains
Generative Adversarial Networks (GANs) based on the source VNF profiling data,
and the trained GANs model is sent to the target VNF domain. Then, FTCA
realizes federated domain adaptation by using the generated source VNF data and
less target VNF profiling data, while keeping the raw data locally. Experiments
show that the proposed FTCA can effectively predict the required resources for
the target VNF. Specifically, the RMSE index of the regression model decreases
by 38.5% and the R-squared metric advances up to 68.6%.

本文提出了一种基于联邦传输组件分析的方法，通过训练生成对抗网络模型并使用已有和目标虚拟网络功能的数据，实现了资源消耗的预测，同时保护数据隐私。实验结果表明，该方法能有效地减少回归模型的均方根误差指标，并提高决定系数度量值。