# Energy-Efficient Federated Learning and Migration in Digital Twin Edge Networks

arXiv: https://arxiv.org/abs/2503.15822 · HTML: https://mlsanity.com/paper/2503.15822/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 57 references that are in the map.
- First posted on arXiv 20 Mar 2025.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Toward an Automated Auction Framework for Wireless Federated Learning Services Market](https://mlsanity.com/paper/1912.06370/) (2019)
- [Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks](https://mlsanity.com/paper/2011.09902/) (2020)
- [Adaptive Federated Learning and Digital Twin for Industrial Internet of Things](https://mlsanity.com/paper/2010.13058/) (2020)
- [A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems](https://mlsanity.com/paper/2212.01351/) (2022)
- [Joint Communication, Computation, Caching, and Control in Big Data Multi-access Edge Computing](https://mlsanity.com/paper/1803.11512/) (2018)
- [Two-Timescale Synchronization and Migration for Digital Twin Networks: A Multi-Agent Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2409.01092/) (2024)
- [Trustworthy DNN Partition for Blockchain-enabled Digital Twin in Wireless IIoT Networks](https://mlsanity.com/paper/2405.17914/) (2024)
- [When Digital Twin Meets 6G: Concepts, Obstacles, and Research Prospects](https://mlsanity.com/paper/2409.02008/) (2024)
- [Age-of-Information and Energy Optimization in Digital Twin Edge Networks](https://mlsanity.com/paper/2409.11799/) (2024)
- [Sustainable Placement with Cost Minimization in Wireless Digital Twin Networks](https://mlsanity.com/paper/2409.12504/) (2024)

## Abstract (arXiv)

The digital twin edge network (DITEN) is a significant paradigm in the sixth-generation wireless system (6G) that aims to organize well-developed infrastructures to meet the requirements of evolving application scenarios. However, the impact of the interaction between the long-term DITEN maintenance and detailed digital twin tasks, which often entail privacy considerations, is commonly overlooked in current research. This paper addresses this issue by introducing a problem of digital twin association and historical data allocation for a federated learning (FL) task within DITEN. To achieve this goal, we start by introducing a closed-form function to predict the training accuracy of the FL task, referring to it as the data utility. Subsequently, we carry out comprehensive convergence analyses on the proposed FL methodology. Our objective is to jointly optimize the data utility of the digital twin-empowered FL task and the energy costs incurred by the long-term DITEN maintenance, encompassing FL model training, data synchronization, and twin migration. To tackle the aforementioned challenge, we present an optimization-driven learning algorithm that effectively identifies optimized solutions for the formulated problem. Numerical results demonstrate that our proposed algorithm outperforms various baseline approaches.