Federated learning (FL) is a promising technique that enables many edge devices to train a
machine learning model collaboratively in wireless networks. By exploiting the superposition
nature of wireless waveforms, over-the-air computation (AirComp) can accelerate model
aggregation and hence facilitate communication-efficient FL. Due to channel fading, power
control is crucial in AirComp. Prior works assume that the signals to be aggregated from
each device, ie, local gradients have identical statistics. In FL, however, gradient statistics …

To enable communication-efficient federated learning, fast model aggregation can be
designed using over-the-air computation (AirComp). In order to implement a reliable and
high-performance AirComp over fading channels, power control at edge devices is crucial.
Existing works assume that the signal to be aggregated from each device is identically
distributed, and normalized with zero mean and unit variance. This assumption, however,
does not hold for gradient aggregation in machine learning because gradient statistics are …