D2D 辅助的双阶段空中计算模型聚合方案

In wireless federated learning, the channel quality heterogeneity of the devices at different locations results in model aggregation errors dominated by the device with the worst channel quality. To address this, a device-to-device (D2D) assisted over-the-air computation (OAC) federated learning scheme is proposed, where the devices with good channel quality are selected to assist edge devices in updating their local models to the server. An optimization problem is formulated to minimize the mean squared error and an alternating optimization algorithm is proposed to optimize the operational parameters of all devices and the base station with low complexity. To assess the performance of the proposed scheme, this paper validates its advantages over traditional OAC-based model aggregation schemes through theoretical analysis. Meanwhile, two different device distribution scenarios are designed to construct federated learning experiments based on neural networks and real datasets. The results show that the proposed algorithm converges quickly. Compared with traditional OAC schemes and existing scheduling or relay-based OAC schemes, the proposed scheme significantly reduces model aggregation errors and improves the prediction accuracy of federated learning.