Independent Split Model Inference at Operator Network for Network Performance Estimation

In telecommunications, information delivery is per-formed over inherently distributed elements such as application servers, packet core network equipment, radio base stations, and mobile user equipment (UE). Predicting key performance indicators (KPIs) for services is important for mobile operators in preventing customer churn. In addition, it is important to achieve accurate estimations, to troubleshoot and localize faults in the end-to-end transmission by collecting data from potentially causal distributed measurement points located on the distributed delivery elements. Due to the regulations for protecting data privacy, there is an increasing interest in decentralized machine learning models including Split Learning (SL). A distributed learning technique such as parallel SL enables accurate model training jointly, but it necessitates all distributed elements to synchronize and correlate on a unique identifier such as sample id, to align the vertically distributed data samples, which causes dependency and high signaling overhead between the collaborating nodes. In this paper, we demonstrate a Translator Model that imputes the remote model parameters based on the local ones in a SL setting. This way, sample alignment overhead during model inference is addressed, and significant reduction in communication cost is achieved.