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：随着知识迁移与数据隐私问题的日益关注，传统网络中的"收集-分析"范式面临挑战。具体而言，虚拟网络功能（VNF）的智能编排需要理解并画像其资源消耗情况。然而，对所有类型VNF进行画像耗时巨大。因此，在保持数据隐私的前提下，将已充分画像的VNF知识迁移至缺乏画像的其他VNF类型至关重要。为此，本文提出一种面向源VNF与目标VNF的联邦迁移成分分析（FTCA）方法。FTCA首先基于源VNF画像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利用生成的源VNF数据与少量目标VNF画像数据实现联邦域适应，同时确保原始数据保留在本地。实验表明，所提出的FTCA能够有效预测目标VNF所需资源。具体而言，回归模型的均方根误差（RMSE）指标降低了38.5%，决定系数（R-squared）指标提升了高达68.6%。