论文标题
通过能量收集设备的空中联合学习
Over-the-Air Federated Learning with Energy Harvesting Devices
论文作者
论文摘要
我们考虑在移动设备中从周围收集所需能量的移动设备之间的联合边缘学习（感觉），并通过共享的无线通道与参数服务器（PS）共享更新。特别是，我们考虑使用无线电（OTA）聚集的能源收集FL，在该聚集中，参与设备只有在拥有所需的能量时才执行本地计算和无线传输，并通过同一通道带宽同时传输本地更新。为了防止异质设备之间的偏见，我们利用了对最新能量到达和数据红衣的加权平均。我们提供收敛分析并进行不同的能量到达曲线的数值实验，这表明，即使提出的方案对无差错方案的异质能量到达的设备具有鲁棒性，但我们观察到能量收集的能源收获ota fl的性能损失5-10％。
We consider federated edge learning (FEEL) among mobile devices that harvest the required energy from their surroundings, and share their updates with the parameter server (PS) through a shared wireless channel. In particular, we consider energy harvesting FL with over-the-air (OTA) aggregation, where the participating devices perform local computations and wireless transmission only when they have the required energy available, and transmit the local updates simultaneously over the same channel bandwidth. In order to prevent bias among heterogeneous devices, we utilize a weighted averaging with respect to their latest energy arrivals and data cardinalities. We provide a convergence analysis and carry out numerical experiments with different energy arrival profiles, which show that even though the proposed scheme is robust against devices with heterogeneous energy arrivals in error-free scenarios, we observe a 5-10% performance loss in energy harvesting OTA FL.
