基于非一致多数投票计算的无线网络分布式学习
Distributed Learning over a Wireless Network with Non-coherent Majority Vote Computation
摘要：
在这项研究中，我们提出了一种空中计算（OAC）方案来计算联邦边缘学习（FEEL）的多数投票（MV）。利用所提出的方法，边缘设备（ED）传输局部随机梯度的符号，即，通过激活两个正交资源中的一个来投票。边缘服务器（ES）处的MV通过利用资源上的累积来利用非相干检测器获得。因此，所提出的方案消除了在ED和ES处对信道状态信息（CSI）的需要。在这项研究中，我们分析了各种梯度编码策略，通过权函数和波形配置在正交频分复用（OFDM）。我们表明，特定的权重函数，使缺席ED（即，缺席者的硬编码参与（HPA））或加权投票（即，软编码参与（SP））可以显著降低检测到不正确MV的概率。通过考虑路径损耗、功率控制、小区大小和衰落信道，我们证明了分布式学习算法对一个非凸函数的收敛性。仿真结果表明，在异构数据分布情况下，当时间同步和功率控制不理想时，采用HPA和SP的方案仍能提供较高的测试精度。
英文摘要：
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.
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