用联邦学习保护隐私，实现移动传输的高效调度。
Federated Learning-Assisted Optimization of Mobile Transmission with Digital Twins
- 通过联邦优化框架与数字孪生交互，不泄露私有信息。
- 实测使任务完成时间减少，带宽/能耗违规接近零。
- 适合边缘计算中需保护用户隐私的资源调度场景。
数字孪生（DT）可保护其对应物理系统的核心隐私信息，如移动设备的移动轨迹、近期位置及信道状况。然而，在线调度器通常依赖此类信息进行带宽共享和时隙分配。本文研究三种受能量约束的传输调度问题：（i）固定功率下最小化总传输时间；（ii）固定速率时隙配合功率控制；（iii）在固定时间内最大化上传数据量。提出实时联邦优化框架，仅与数字孪生交互即可生成全局分数解，无需暴露私有信息。随后采用依赖性取整将分数解转为实际信道调度。实验表明，在典型边缘服务器硬件上，端到端延迟为毫秒级，任务完成时间显著缩短，带宽与能量违反几乎为零。据我们所知，这是首个在不暴露私有数据的前提下实现跨数字孪生信道共享的框架。
原文摘要 · Abstract (English)
A Digital Twin (DT) may protect information that is considered private to its associated physical system. For a mobile device, this may include its mobility profile, recent location(s), and experienced channel conditions. Online schedulers, however, typically use this type of information to perform tasks such as shared bandwidth and channel time slot assignments. In this paper, we consider three transmission scheduling problems with energy constraints, where such information is needed, and yet must remain private: minimizing total transmission time when (i) fixed-power or (ii) fixed-rate time slotting with power control is used, and (iii) maximizing the amount of data uploaded in a fixed time period. Using a real-time federated optimization framework, we show how the scheduler can iteratively interact only with the DTs to produce global fractional solutions to these problems, without the latter revealing their private information. Then dependent rounding is used to round the fractional solution into a channel transmission schedule for the physical systems. Experiments show consistent makespan reductions with near-zero bandwidth/energy violations and millisecond-order end-to-end runtime for typical edge server hardware. To the best of our knowledge, this is the first framework that enables channel sharing across DTs using operations that do not expose private data.
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