论文标题
基于群集的合作数字数字汇总，用于无线联合边缘学习
Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learning
论文作者
论文摘要
在本文中，我们研究了使用无线计算（AIRCOMP）的无线边缘的联合学习系统。在这样的系统中，用户同时通过多访问通道传输他们的消息，以实现快速模型聚合。最近，已经提出了一种基于数字调制的AIRCOMP方案，该方案具有一位梯度量化和用户的截断通道倒置，以及在Fusion Center（FC）的大多数投票解码器。我们提出了一项改进的数字AIRCOMP计划，以放松其对发射机的要求，在该机构中，用户可以进行相位校正并全力传输。为了表征FC处的解码故障概率，我们介绍了标准化的检测信噪比（SNR），可以将其解释为用户的有效参与率。为了减轻无线褪色，我们进一步提出了一个基于群集的系统，并根据标准化检测SNR设计继电器选择方案。通过每个集群和继电器选择中的本地数据融合，我们的方案可以完全利用空间多样性，以增加有效的投票用户数量并加速模型收敛。
In this paper, we study a federated learning system at the wireless edge that uses over-the-air computation (AirComp). In such a system, users transmit their messages over a multi-access channel concurrently to achieve fast model aggregation. Recently, an AirComp scheme based on digital modulation has been proposed featuring one-bit gradient quantization and truncated channel inversion at users and a majority-voting based decoder at the fusion center (FC). We propose an improved digital AirComp scheme to relax its requirements on the transmitters, where users perform phase correction and transmit with full power. To characterize the decoding failure probability at the FC, we introduce the normalized detection signal-to-noise ratio (SNR), which can be interpreted as the effective participation rate of users. To mitigate wireless fading, we further propose a cluster-based system and design the relay selection scheme based on the normalized detection SNR. By local data fusion within each cluster and relay selection, our scheme can fully exploit spatial diversity to increase the effective number of voting users and accelerate model convergence.
