论文标题
用于联合边缘学习的多电池非连接的无天线计算
Multi-cell Non-coherent Over-the-Air Computation for Federated Edge Learning
论文作者
论文摘要
在本文中，我们提出了一个框架，在该框架中，在上行链路（UL）和下行链路（DL）中都会在多单元环境中进行过链接（UL）和下行链路（DL），以解决联合边缘学习（Feel）的延迟和可伸缩性问题。为了消除边缘设备（EDS）和边缘服务器（ESS）的通道状态信息（CSI），并放大OAC的时间同步需求，我们使用非连锁计算方案，即基于频率迁移键合（FSK）基于频率转移（FSK）的多数票（MV）（FSK-MV）（FSK-MV）。通过提出的框架，多重ESS函数是UL中的聚集节点，并且每个ES都独立确定MVS。 ESS广播检测到的MVS之后，埃德斯通过DL中的另一个OAC确定了梯度的符号。因此，对于OAC，利用细胞间干扰。在这项研究中，我们证明了与拟议的OAC框架为感觉的非凸优化问题的收敛性。我们还通过比较均质和异质数据分布的多细胞和单细胞方案的测试准确性来评估所提出方法的疗效。
In this paper, we propose a framework where over-the-air computation (OAC) occurs in both uplink (UL) and downlink (DL), sequentially, in a multi-cell environment to address the latency and the scalability issues of federated edge learning (FEEL). To eliminate the channel state information (CSI) at the edge devices (EDs) and edge servers (ESs) and relax the time-synchronization requirement for the OAC, we use a non-coherent computation scheme, i.e., frequency-shift keying (FSK)-based majority vote (MV) (FSK-MV). With the proposed framework, multiple ESs function as the aggregation nodes in the UL and each ES determines the MVs independently. After the ESs broadcast the detected MVs, the EDs determine the sign of the gradient through another OAC in the DL. Hence, inter-cell interference is exploited for the OAC. In this study, we prove the convergence of the non-convex optimization problem for the FEEL with the proposed OAC framework. We also numerically evaluate the efficacy of the proposed method by comparing the test accuracy in both multi-cell and single-cell scenarios for both homogeneous and heterogeneous data distributions.
