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%.

翻译：知识迁移与数据隐私日益受到关注，这对网络中传统的"收集-分析"范式提出了挑战。具体而言，虚拟网络功能（VNF）的智能编排需要理解并分析其资源消耗模式。然而，对所有类型的VNF进行配置分析耗时巨大。因此，在保持数据隐私的前提下，将已充分配置的VNF知识迁移至缺乏配置的其他VNF类型显得尤为重要。为此，本文提出了一种在源VNF与目标VNF之间进行联邦迁移成分分析（FTCA）的方法。FTCA首先基于源VNF配置数据训练生成对抗网络（GAN），并将训练好的GAN模型发送至目标VNF域。随后，FTCA通过利用生成的源VNF数据与少量目标VNF配置数据实现联邦域自适应，同时将原始数据保留在本地。实验表明，所提出的FTCA方法能够有效预测目标VNF所需的资源。具体而言，回归模型的均方根误差（RMSE）指标降低了38.5%，R平方指标提升了68.6%。