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.

翻译：数字孪生（DT）可保护与其关联物理系统相关的隐私信息。对移动设备而言，这类信息可能包括其移动轨迹、近期位置及经历的信道条件。然而，在线调度器通常需要利用此类信息执行共享带宽与信道时隙分配等任务。本文研究了三个需要此类隐私信息但必须保持其私密性的能量约束传输调度问题：在使用（i）固定功率或（ii）功率控制的固定速率时隙分配时最小化总传输时间，以及（iii）在固定时间段内最大化数据上传量。通过实时联邦优化框架，我们展示了调度器如何仅与数字孪生进行迭代交互，从而为这些问题生成全局分数解，且后者无需暴露其隐私信息。随后采用依赖舍入法将分数解转化为物理系统的信道传输调度方案。实验表明，在典型边缘服务器硬件上，该方法能持续缩短完工时间，带宽/能量违规率接近零，端到端运行时间达毫秒级。据我们所知，这是首个通过非暴露隐私数据的操作实现跨数字孪生信道共享的框架。