Over-the-air computation (OAC) is a promising technique to achieve fast model aggregation across multiple devices in federated edge learning (FEEL). In addition to the analog schemes, one-bit digital aggregation (OBDA) scheme was proposed to adapt OAC to modern digital wireless systems. However, one-bit quantization in OBDA can result in a serious information loss and slower convergence of FEEL. To overcome this limitation, this paper proposes an unsourced massive access (UMA)-based generalized digital OAC (GD-OAC) scheme. Specifically, at the transmitter, all the devices share the same non-orthogonal UMA codebook for uplink transmission. The local model update of each device is quantized based on the same quantization codebook. Then, each device transmits a sequence selected from the UMA codebook based on the quantized elements of its model update. At the receiver, we propose an approximate message passing-based algorithm for efficient UMA detection and model aggregation. Simulation results show that the proposed GD-OAC scheme significantly accelerates the FEEL convergences compared with the state-of-the-art OBDA scheme while using the same uplink communication resources.

翻译：空中计算（OAC）是一种在联邦边缘学习（FEEL）中实现多设备快速模型聚合的前沿技术。除模拟方案外，文献还提出单比特数字聚合（OBDA）方案，使OAC适配现代数字无线系统。然而，OBDA中的单比特量化会导致严重信息损失并降低FEEL收敛速度。为克服此局限，本文提出基于无源海量接入（UMA）的广义数字空中计算（GD-OAC）方案。具体而言，在发送端，所有设备共享相同的非正交UMA码本进行上行传输。每个设备的本地模型更新基于相同量化码本进行量化，随后各设备根据其模型更新的量化元素，从UMA码本中选择序列进行传输。在接收端，我们提出基于近似消息传递的高效UMA检测与模型聚合算法。仿真结果表明，在采用相同上行通信资源条件下，所提GD-OAC方案相较于现有最优OBDA方案能显著加速FEEL收敛进程。