Biased Over-the-Air Federated Learning under Wireless Heterogeneity
Biased Over-the-Air Federated Learning under Wireless Heterogeneity
Recently, Over-the-Air (OTA) computation has emerged as a promising federated learning (FL) paradigm that leverages the waveform superposition properties of the wireless channel to realize fast model updates. Prior work focused on the OTA device ``pre-scaler" design under \emph{homogeneous} wireless conditions, in which devices experience the same average path loss, …