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（AirComp）已被证明是一种能有效提升联邦边缘学习（FEEL）效率的传输方案。然而，现有采用AirComp方案的FEEL系统在每个全局轮次中常使用传统同步聚合机制进行本地模型聚合，这会面临掉队者问题。本文提出一种基于AirComp方案的半异步聚合FEEL机制（PAOTA），以改善在数据和设备高度异质场景下FEEL系统的训练效率。通过考虑边缘设备模型更新的陈旧度与发散度，最小化每个聚合周期内FEEL全局模型的收敛上界。仿真结果表明，所提算法可实现接近理想本地SGD的收敛性能。此外，在相同目标精度下，PAOTA所需的训练时间少于理想本地SGD和基于AirComp的同步FEEL算法。