论文标题
通过无线网络的分布式学习，具有非协会的多数投票计算
Distributed Learning over a Wireless Network with Non-coherent Majority Vote Computation
论文作者
论文摘要
在这项研究中，我们提出了一个空中计算（OAC）方案，以计算联邦边缘学习（Feel）的多数投票（MV）。通过提出的方法，Edge设备（EDS）通过激活两个正交资源之一来传递局部随机梯度的迹象，即投票。 Edge Server（ES）的MV是通过非连锁检测器获得的，通过利用资源上的积累来获得。因此，提出的方案消除了在EDS和ES上对通道状态信息（CSI）的需求。在这项研究中，我们通过重量函数和波形配置（OFDM）分析了各种梯度编码策略。我们表明，可以使缺席EDS的特定权重函数（即具有缺失者（HPA））或加权投票（即软编码的参与（SP））可以大大降低检测不正确的MV的可能性。通过考虑路径损失，功率控制，细胞大小和褪色通道，我们证明了HPA非凸功能的分布式学习的收敛性。通过模拟，我们表明，即使在异质数据分布场景下，使用HPA和SP的提议方案也可以提供高测试精度。
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.
