# Edge Continual Learning for Dynamic Digital Twins over Wireless Networks

arXiv: https://arxiv.org/abs/2204.04795 · HTML: https://mlsanity.com/paper/2204.04795/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 10 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence](https://mlsanity.com/paper/1801.10112/) (2018)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions](https://mlsanity.com/paper/2102.12169/) (2021)
- [Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks](https://mlsanity.com/paper/2011.09902/) (2020)
- [Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective](https://mlsanity.com/paper/2105.01696/) (2021)

## Built on by (most cited first, 13 of 13)

- [Less Data, More Knowledge: Building Next Generation Semantic Communication Networks](https://mlsanity.com/paper/2211.14343/) (2022)
- [Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G](https://mlsanity.com/paper/2405.02336/) (2024)
- [A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems](https://mlsanity.com/paper/2212.01351/) (2022)
- [Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network](https://mlsanity.com/paper/2210.06649/) (2022)
- [The Seven Worlds and Experiences of the Wireless Metaverse: Challenges and Opportunities](https://mlsanity.com/paper/2304.10282/) (2023)
- [How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks](https://mlsanity.com/paper/2507.07067/) (2025)
- [Internet of Federated Digital Twins (IoFDT): Connecting Twins Beyond Borders for Society 5.0](https://mlsanity.com/paper/2312.06432/) (2023)
- [Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning](https://mlsanity.com/paper/2406.15819/) (2024)
- [Continual Deep Reinforcement Learning to Prevent Catastrophic Forgetting in Jamming Mitigation](https://mlsanity.com/paper/2410.10521/) (2024)
- [Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks](https://mlsanity.com/paper/2502.05116/) (2025)
- [Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning](https://mlsanity.com/paper/2210.05582/) (2022)
- [Analysis of the Memorization and Generalization Capabilities of AI Agents: Are Continual Learners Robust?](https://mlsanity.com/paper/2309.10149/) (2023)
- [Optimizing Reinforcement Learning Training over Digital Twin Enabled Multi-fidelity Networks](https://mlsanity.com/paper/2603.08931/) (2026)

## Abstract (arXiv)

Digital twins (DTs) constitute a critical link between the real-world and the metaverse. To guarantee a robust connection between these two worlds, DTs should maintain accurate representations of the physical applications, while preserving synchronization between real and digital entities. In this paper, a novel edge continual learning framework is proposed to accurately model the evolving affinity between a physical twin (PT) and its corresponding cyber twin (CT) while maintaining their utmost synchronization. In particular, a CT is simulated as a deep neural network (DNN) at the wireless network edge to model an autonomous vehicle traversing an episodically dynamic environment. As the vehicular PT updates its driving policy in each episode, the CT is required to concurrently adapt its DNN model to the PT, which gives rise to a de-synchronization gap. Considering the history-aware nature of DTs, the model update process is posed a dual objective optimization problem whose goal is to jointly minimize the loss function over all encountered episodes and the corresponding de-synchronization time. As the de-synchronization time continues to increase over sequential episodes, an elastic weight consolidation (EWC) technique that regularizes the DT history is proposed to limit de-synchronization time. Furthermore, to address the plasticity-stability tradeoff accompanying the progressive growth of the EWC regularization terms, a modified EWC method that considers fair execution between the historical episodes of the DTs is adopted. Ultimately, the proposed framework achieves a simultaneously accurate and synchronous CT model that is robust to catastrophic forgetting. Simulation results show that the proposed solution can achieve an accuracy of 90 % while guaranteeing a minimal desynchronization time.