Federated Transfer Component Analysis Towards Effective VNF Profiling

TL;DR

This paper introduces a federated transfer learning method using GANs for efficient VNF profiling, enabling resource prediction with privacy preservation and improved accuracy.

Contribution

It proposes a novel FTCA approach combining federated domain adaptation and GANs for VNF profiling transfer learning.

Findings

RMSE decreased by 38.5%

R-squared improved up to 68.6%

Effective resource prediction for target VNFs

Abstract

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%.