Network Digital Untwinning: Towards Backward Optimization of Digital Twins

2026-04-30

AI Summary

This work addresses the challenge of selectively removing specific data contributions from network digital twins—arising from device decommissioning, network reconfiguration, or compliance requirements—without compromising model integrity. The authors propose a novel “network digital de-twinning” framework that introduces, for the first time, a reversible deletion mechanism supporting both single-point and batch operations. The framework provides theoretical guarantees that the resulting model is indistinguishable from one trained from scratch without the deleted data. It identifies target twin instances by leveraging geographic proximity, data distribution, and network attributes, and achieves precise deletion through connectivity-aware rollback checkpoint selection, Gaussian noise injection, and remapping strategies. Furthermore, an attribute-clustering-based parallel scheduling algorithm is designed to enhance computational efficiency. Experiments on real-world network traffic data demonstrate that the method effectively removes targeted data while preserving model performance and structural integrity.

Abstract

Network digital twins (NDTs) are transforming network management by offering precise virtual replicas of physical network systems. However, their reliance on diverse and sensitive data introduces significant challenges related to data management, regulatory compliance, and user privacy. In scenarios where selective data removal is necessary, such as device deactivation, network reconfiguration, or regulatory compliance, traditional approaches often fall short of preserving the integrity of the twin model. To address this gap, we introduce a network digital untwinning framework that enables the targeted removal of deprecated NDT contributions while maintaining model integrity. Our approach comprises two complementary components: Single Request Untwinning (\algO) and Parallel Request Untwinning (\algM) mechanisms. \algO leverages connectivity metrics based on geographical proximity, data distribution, and network-level attributes to identify and remove the target NDT along with its propagating influence. This is achieved through an optimally selected rollback checkpoint augmented with injected Gaussian noise, followed by a precise remapping phase. \algM extends this mechanism to efficiently handle multiple removal requests by clustering NDTs with similar attributes and performing a coordinated rollback and untwinning schedule. We provide theoretical guarantees on model indistinguishability from scratch-built twins, and validate the framework through extensive experiments on real-world traffic data, demonstrating its effectiveness and operational efficiency.

Problem

• Digital Twin
• Data Removal
• Model Integrity
• Network Management
• Privacy Compliance

Innovation

• Digital Twin
• Untwinning
• Backward Optimization
• Model Integrity
• Privacy-Preserving