Vertical Split Learning-Based Identification and Explainable Deep Learning-Based Localization of Failures in Multi-Domain NFV Systems

Automated failure management in Network Function Virtualization (NFV) systems continues to gain significant attention as it allows identifying and mitigating failures in a timely manner, ensuring continuous and stable operation of services. In multi-domain systems, where services are provisioned across multiple domains, each domain is managed by a unique single-domain orchestrator (SDO), the problem of automated NFV failure management takes another dimension as it requires a privacy-preserving collaboration among the SDOs. This is due to the fact that SDOs are not willing to share private and business-critical information of their network to different parties. In this paper, we focus on the problem of failure identification and localization in NFV systems in multi-domain networks where SDOs collaborate, in a distributed privacy-preserving learning scheme, to train a single neural network without sharing any raw data. To this end, we propose a Vertical Split Learning (VSL)-based approach with a client-server architecture for failure identification and localization over vertically partitioned data. Additionally, we utilize Explainable Deep Learning (XDL) frameworks, namely Integrated Gradients and DeepLIFT, on the failure identification server model to locate the failures without accessing the original data or features and without training a separate localization model. We compare our approach to centralized baseline approaches, and illustrative numerical results show that our proposed solution preserves a performance close to the one achievable with a centralized approach and localizes failures with an accuracy of 83% without the necessity of training a new localization model.