# Synergizing AI and Digital Twins for Next-Generation Network Optimization, Forecasting, and Security

arXiv: https://arxiv.org/abs/2503.06302 · HTML: https://mlsanity.com/paper/2503.06302/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 8 Mar 2025.

## Builds on (most cited first, 9 of 9)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems](https://mlsanity.com/paper/2203.01387/) (2022)
- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [Network Digital Twin: Context, Enabling Technologies and Opportunities](https://mlsanity.com/paper/2205.14206/) (2022)
- [Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challenges](https://mlsanity.com/paper/2406.07857/) (2024)
- [Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks](https://mlsanity.com/paper/2407.00286/) (2024)
- [Securing Distributed Network Digital Twin Systems Against Model Poisoning Attacks](https://mlsanity.com/paper/2407.01917/) (2024)

## Built on by (most cited first, 3 of 3)

- [Network Digital Untwinning: Towards Backward Optimization of Digital Twins](https://mlsanity.com/paper/2605.00169/) (2026)
- [On Transferring, Merging, and Splitting Task-Oriented Network Digital Twins](https://mlsanity.com/paper/2509.02551/) (2025)
- [Customized Generative AI Agent for Transportation Engineering Practice: A Development and Continued Pre-training Guideline](https://mlsanity.com/paper/2606.29014/) (2026)

## Abstract (arXiv)

Digital network twins (DNTs) are virtual representations of physical networks, designed to enable real-time monitoring, simulation, and optimization of network performance. When integrated with machine learning (ML) techniques, particularly federated learning (FL) and reinforcement learning (RL), DNTs emerge as powerful solutions for managing the complexities of network operations. This article presents a comprehensive analysis of the synergy of DNTs, FL, and RL techniques, showcasing their collective potential to address critical challenges in 6G networks. We highlight key technical challenges that need to be addressed, such as ensuring network reliability, achieving joint data-scenario forecasting, and maintaining security in high-risk environments. Additionally, we propose several pipelines that integrate DNT and ML within coherent frameworks to enhance network optimization and security. Case studies demonstrate the practical applications of our proposed pipelines in edge caching and vehicular networks. In edge caching, the pipeline achieves over 80% cache hit rates while balancing base station loads. In autonomous vehicular system, it ensure a 100% no-collision rate, showcasing its reliability in safety-critical scenarios. By exploring these synergies, we offer insights into the future of intelligent and adaptive network systems that automate decision-making and problem-solving.