In this study, we propose an over-the-air computation (OAC) scheme to calculate the majority vote (MV) for federated edge learning (FEEL). With the proposed approach, edge devices (EDs) transmit the signs of local stochastic gradients, i.e., votes, by activating one of two orthogonal resources. The MVs at the edge server (ES) are obtained with non-coherent detectors by exploiting the accumulations on the resources. Hence, the proposed scheme eliminates the need for channel state information (CSI) at the EDs and ES. In this study, we analyze various gradient-encoding strategies through the weight functions and waveform configurations over orthogonal frequency division multiplexing (OFDM). We show that specific weight functions that enable absentee EDs (i.e., hard-coded participation with absentees (HPA)) or weighted votes (i.e., soft-coded participation (SP)) can substantially reduce the probability of detecting the incorrect MV. By taking path loss, power control, cell size, and fading channel into account, we prove the convergence of the distributed learning for a non-convex function for HPA. Through simulations, we show that the proposed scheme with HPA and SP can provide high test accuracy even when the time-synchronization and the power control are not ideal under heterogeneous data distribution scenarios.

翻译：在此研究中,我们提议了一个超空计算(OAC)办法,用于计算联邦边缘学习(FEEL)的多数票(MV ) 。 使用拟议方法, 边缘装置(EDs) 传递当地随机梯度的信号, 即通过激活两种正方位资源之一的选票。 边缘服务器(ES) 的MV 是通过非一致检测器获得的, 利用资源累积情况获得的。 因此, 拟议的办法消除了在经销协议和ES 中频道国家信息(CSI ) 的需要。 在这次研究中, 我们分析各种梯度编码战略,通过重量函数和波形配置超过或正方位频率分数多x(OFDM), 以传递当地随机梯度梯度梯度梯度梯度梯度梯度梯度梯度的信号。 我们显示,具体重量功能可以让缺席 EDME( HA) 使用硬码参与 ) 或加权票( 软码参与 (SP) ) 大大降低探测错误 mVA 概率的可能性。, 即便采用非路径丢失、 权力控制、 单元格大小和 流流流化规则 显示 HPA 系统测试功能, 我们学习HSBA 系统 系统, 可以提供H 测试计划 的系统, 我们学习H 系统 的系统 系统, 测试 的系统, 可以, 可以 。