Network Digital Untwinning: Towards Backward Optimization of Digital Twins
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917452200280064 |
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| author | Zhang, Zifan Chen, Dianwei Gao, Anjun Wang, Manhua Chen, Mingzhe Fang, Minghong Yang, Xianfeng Liu, Yuchen |
| author_facet | Zhang, Zifan Chen, Dianwei Gao, Anjun Wang, Manhua Chen, Mingzhe Fang, Minghong Yang, Xianfeng Liu, Yuchen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00169 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Network Digital Untwinning: Towards Backward Optimization of Digital Twins Zhang, Zifan Chen, Dianwei Gao, Anjun Wang, Manhua Chen, Mingzhe Fang, Minghong Yang, Xianfeng Liu, Yuchen Networking and Internet Architecture Distributed, Parallel, and Cluster Computing Machine Learning 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. |
| title | Network Digital Untwinning: Towards Backward Optimization of Digital Twins |
| topic | Networking and Internet Architecture Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2605.00169 |