  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.
