Federated Learning (FL) with over-the-air computation is susceptible to analog aggregation error due to channel conditions and noise. Excluding devices with weak channels can reduce the aggregation error, but also decreases the amount of training data in FL. In this work, we jointly design the uplink receiver beamforming and device selection in over-the-air FL to maximize the training convergence rate. We propose a new method termed JBFDS, which takes into account the impact of receiver beamforming and device selection on the global loss function at each training round. Our simulation results with real-world image classification demonstrate that the proposed method achieves faster convergence with significantly lower computational complexity than existing alternatives.
翻译:由于频道条件和噪音,采用超空计算法的联邦学习联盟(FL)容易发生模拟汇总错误,因为频道条件和噪音而导致模拟汇总错误。排除带弱频道的装置可以减少聚合错误,但也减少FL培训数据的数量。在这项工作中,我们联合设计了超空FL的上链接收器束和装置选择,以最大限度地提高培训趋同率。我们提出了一个名为JBFDS的新方法,其中考虑到每轮培训中接收器束和装置选择对全球损失功能的影响。我们用真实世界图像分类进行的模拟结果显示,拟议方法比现有替代方法的计算复杂性要低得多。</s>