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能够有效提升联邦边缘学习效率的传输方案。然而，现有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采用传统同步聚合机制进行本地模型聚合，这会导致掉队者问题。本文提出一种基于空中计算方案（PAOTA）的半异步聚合联邦边缘学习机制，以提升在数据和设备存在显著异构性情况下的联邦边缘学习系统训练效率。通过考虑边缘设备模型更新的陈旧性与差异度，我们通过调整每个聚合周期内边缘设备的上行传输功率来最小化联邦边缘学习全局模型的收敛上界。仿真结果表明，所提算法能够达到接近理想局部随机梯度下降法的收敛性能。此外，在相同目标精度下，PAOTA所需的训练时间均少于理想局部随机梯度下降法与基于空中计算的同步联邦边缘学习算法。