Update Estimation and Scheduling for Over-the-Air Federated Learning with Energy Harvesting Devices
Furkan Bagci, Busra Tegin, Mohammad Kazemi, Tolga M. Duman
我们研究无线(OTA)联合学习(FL),用于通过无线褪色多通道(MAC)进行异质数据分发的能量收集设备。 为了解决低能量到达和数据异质性对全球学习的影响,我们提出了用户调度策略。 具体来说,我们开发两种方法:1)基于熵的已知数据分布调度,2)基于最小二乘的用户表示估计,用于在参数服务器上使用未知数据分布进行调度。 这两种方法都旨在选择不同的用户,减轻偏见和加强融合。 数值和分析结果通过减少冗余和节约能量证明学习成绩提高。
We study over-the-air (OTA) federated learning (FL) for energy harvesting devices with heterogeneous data distribution over wireless fading multiple access channel (MAC). To address the impact of low energy arrivals and data heterogeneity on global learning, we propose user scheduling strategies. Specifically, we develop two approaches: 1) entropy-based scheduling for known data distributions and 2) least-squares-based user representation estimation for scheduling with unknown data distributions...
