论文标题
基于平衡数字系统的空中计算用于联合边缘学习
Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learning
论文作者
论文摘要
在这项研究中，我们提出了一个数字空中计算（OAC）方案，用于实现联合边缘学习（FEES）的连续值（模拟）聚合。我们显示，可以使用相应的数字的平均值来计算一组实价参数的平均值，其中数字是根据平衡数字系统获得的。通过利用此关键属性，提出的方案将局部随机梯度编码为一组数字。接下来，它通过使用数字的值来确定激活的正交频施加（OFDM）子载体的位置。为了消除需要精确样本级时同步，通道估计开销和通道反转的需求，该提议的方案还使用边缘服务器（ES）的非连接接收器，并且不利用边缘设备（EDS）的预先平等。我们理论上分析了提出的方案的MSE性能和非凸损耗函数的收敛速率。为了通过提出的方案提高感觉的测试精度，我们介绍了自适应绝对最大值（AAM）的概念。我们的数值结果表明，当提出的方案与AAM一起使用时，测试精度可达到98％的异质数据分布。
In this study, we propose a digital over-the-air computation (OAC) scheme for achieving continuous-valued (analog) aggregation for federated edge learning (FEEL). We show that the average of a set of real-valued parameters can be calculated approximately by using the average of the corresponding numerals, where the numerals are obtained based on a balanced number system. By exploiting this key property, the proposed scheme encodes the local stochastic gradients into a set of numerals. Next, it determines the positions of the activated orthogonal frequency division multiplexing (OFDM) subcarriers by using the values of the numerals. To eliminate the need for precise sample-level time synchronization, channel estimation overhead, and channel inversion, the proposed scheme also uses a non-coherent receiver at the edge server (ES) and does not utilize a pre-equalization at the edge devices (EDs). We theoretically analyze the MSE performance of the proposed scheme and the convergence rate for a non-convex loss function. To improve the test accuracy of FEEL with the proposed scheme, we introduce the concept of adaptive absolute maximum (AAM). Our numerical results show that when the proposed scheme is used with AAM for FEEL, the test accuracy can reach up to 98% for heterogeneous data distribution.
