针对能量采集设备的联邦学习，提出用户调度策略提升收敛速度
Update Estimation and Scheduling for Over-the-Air Federated Learning with Energy Harvesting Devices
- 基于熵或最小二乘法估计用户数据特征，选择多样性用户
- 减少冗余更新，提升学习性能并节省能源
- 适合资源受限的无线边缘联邦学习场景
我们研究了在无线衰落多址信道上，针对数据异构的能量采集设备的过空气联邦学习问题。为缓解低能量供应和数据异构对全局学习的影响，提出了用户调度策略。具体包括：1）已知数据分布时采用基于熵的调度；2）未知分布时使用基于最小二乘的用户表示估计进行调度。两种方法均旨在选择多样化用户，降低偏差并加速收敛。数值与分析结果表明，该策略可有效减少冗余、节约能量，并提升学习性能。
原文摘要 · Abstract (English)
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 at the parameter server. Both methods aim to select diverse users, mitigating bias and enhancing convergence. Numerical and analytical results demonstrate improved learning performance by reducing redundancy and conserving energy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。