Over-the-air Computation (AirComp) has been demonstrated as an effective transmission scheme to boost the efficiency of federated edge learning (FEEL). However, existing FEEL systems with AirComp scheme often employ traditional synchronous aggregation mechanisms for local model aggregation in each global round, which suffer from the stragglers issues. In this paper, we propose a semi-asynchronous aggregation FEEL mechanism with AirComp scheme (PAOTA) to improve the training efficiency of the FEEL system in the case of significant heterogeneity in data and devices. Taking the staleness and divergence of model updates from edge devices into consideration, we minimize the convergence upper bound of the FEEL global model by adjusting the uplink transmit power of edge devices at each aggregation period. The simulation results demonstrate that our proposed algorithm achieves convergence performance close to that of the ideal Local SGD. Furthermore, with the same target accuracy, the training time required for PAOTA is less than that of the ideal Local SGD and the synchronous FEEL algorithm via AirComp.

翻译：空中计算已被证明是一种提升联邦边缘学习效率的有效传输方案。然而，现有的采用空中计算方案的联邦边缘学习系统通常在每个全局轮次中使用传统的同步聚合机制进行本地模型聚合，这会受到掉队者问题的困扰。本文提出了一种采用空中计算方案的半异步聚合联邦边缘学习机制，旨在改善设备和数据异质性显著场景下的训练效率。通过考虑边缘设备模型更新的陈旧度和差异度，我们在每个聚合周期调整边缘设备的上行发射功率，以最小化联邦边缘学习全局模型的收敛上界。仿真结果表明，所提出的算法实现了接近理想本地随机梯度下降的收敛性能。此外，在达到相同目标精度的情况下，PAOTA所需的训练时间少于理想本地随机梯度下降和基于空中计算的同步联邦边缘学习算法。