# On Transferring, Merging, and Splitting Task-Oriented Network Digital Twins

arXiv: https://arxiv.org/abs/2509.02551 · HTML: https://mlsanity.com/paper/2509.02551/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 2 Sep 2025.

## Builds on (most cited first, 17 of 17)

- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Multimodal Learning with Transformers: A Survey](https://mlsanity.com/paper/2206.06488/) (2022)
- [Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2102.04830/) (2021)
- [What Makes Multi-modal Learning Better than Single (Provably)](https://mlsanity.com/paper/2106.04538/) (2021)
- [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019)
- [More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification](https://mlsanity.com/paper/2008.05457/) (2020)
- [Convergence Time Optimization for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2001.07845/) (2020)
- [Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities](https://mlsanity.com/paper/2202.02559/) (2022)
- [Network Digital Twin: Context, Enabling Technologies and Opportunities](https://mlsanity.com/paper/2205.14206/) (2022)
- [A Digital Twin Framework for Physical-Virtual Integration in V2X-Enabled Connected Vehicle Corridors](https://mlsanity.com/paper/2410.00356/) (2024)
- [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)
- [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)
- [Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security](https://mlsanity.com/paper/2503.06302/) (2025)

## Abstract (arXiv)

The integration of digital twinning technologies is driving next-generation networks toward new capabilities, allowing operators to thoroughly understand network conditions, efficiently analyze valuable radio data, and innovate applications through user-friendly, immersive interfaces. Building on this foundation, network digital twins (NDTs) accurately depict the operational processes and attributes of network infrastructures, facilitating predictive management through real-time analysis and measurement. However, constructing precise NDTs poses challenges, such as integrating diverse data sources, mapping necessary attributes from physical networks, and maintaining scalability for various downstream tasks. Unlike previous works that focused on the creation and mapping of NDTs from scratch, we explore intra- and inter-operations among NDTs within a Unified Twin Transformation (UTT) framework, which uncovers a new computing paradigm for efficient transfer, merging, and splitting of NDTs to create task-oriented twins. By leveraging joint multi-modal and distributed mapping mechanisms, UTT optimizes resource utilization and reduces the cost of creating NDTs, while ensuring twin model consistency. A theoretical analysis of the distributed mapping problem is conducted to establish convergence bounds for this multi-modal gated aggregation process. Evaluations on real-world twin-assisted applications, such as trajectory reconstruction, human localization, and sensory data generation, demonstrate the feasibility and effectiveness of interoperability among NDTs for corresponding task development.