# Network Digital Untwinning: Towards Backward Optimization of Digital Twins

arXiv: https://arxiv.org/abs/2605.00169 · HTML: https://mlsanity.com/paper/2605.00169/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 50 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 18 of 18)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [Federated Unlearning: How to Efficiently Erase a Client in FL?](https://mlsanity.com/paper/2207.05521/) (2022)
- [Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities](https://mlsanity.com/paper/2202.02559/) (2022)
- [6G Digital Twin Networks: From Theory to Practice](https://mlsanity.com/paper/2212.02032/) (2022)
- [Wireless Network Digital Twin for 6G: Generative AI as A Key Enabler](https://mlsanity.com/paper/2311.17451/) (2023)
- [Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept](https://mlsanity.com/paper/2402.12682/) (2024)
- [Network Digital Twin: Context, Enabling Technologies and Opportunities](https://mlsanity.com/paper/2205.14206/) (2022)
- [Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks](https://mlsanity.com/paper/2407.00286/) (2024)
- [Digital Network Twins for Next-generation Wireless: Creation, Optimization, and Challenges](https://mlsanity.com/paper/2410.18002/) (2024)
- [Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2401.08216/) (2024)
- [Colosseum as a Digital Twin: Bridging Real-World Experimentation and Wireless Network Emulation](https://mlsanity.com/paper/2303.17063/) (2023)
- [Mapping Wireless Networks into Digital Reality through Joint Vertical and Horizontal Learning](https://mlsanity.com/paper/2404.14497/) (2024)
- [Securing Distributed Network Digital Twin Systems Against Model Poisoning Attacks](https://mlsanity.com/paper/2407.01917/) (2024)
- [Joint Vehicle Connection and Beamforming Optimization in Digital Twin Assisted Integrated Sensing and Communication Vehicular Networks](https://mlsanity.com/paper/2408.00248/) (2024)
- [Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security](https://mlsanity.com/paper/2503.06302/) (2025)
- [VaN3Twin: the Multi-Technology V2X Digital Twin with Ray-Tracing in the Loop](https://mlsanity.com/paper/2505.14184/) (2025)
- [Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework](https://mlsanity.com/paper/2503.04170/) (2025)

## Built on by (most cited first, 1 of 1)

- [CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drives](https://mlsanity.com/paper/2608.01725/) (2026)

## Abstract (arXiv)

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.