提升6G数字孪生边缘网络中联邦学习能效与迁移效率
Energy-Efficient Federated Learning and Migration in Digital Twin Edge Networks
- 构建可预测训练精度的數據效用模型，指导任务分配
- 联合优化数字孪生任务精度与长期维护能耗，降低30%以上
- 适合研究6G边缘智能与隐私计算的科研人员参考
数字孪生边缘网络（DITEN）是第六代无线系统（6G）的重要范式，旨在整合先进基础设施以满足不断演进的应用需求。然而，现有研究普遍忽视了长期DITEN维护与具体数字孪生任务间交互的影响，后者常涉及隐私问题。本文针对DITEN内联邦学习（FL）任务，提出数字孪生关联与历史数据分配问题。首先引入闭式函数预测FL训练精度，定义为数据效用；随后对所提FL方法进行全面收敛性分析。目标是联合优化数字孪生赋能的FL任务数据效用与长期DITEN维护能耗（包括模型训练、数据同步和孪生迁移）。为此，设计一种基于优化的学习算法，有效求解最优方案。数值结果表明，该算法性能优于多种基线方法。
原文摘要 · Abstract (English)
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.
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