Edge federated learning (FL) is an emerging paradigm that trains a global parametric model
from distributed datasets based on wireless communications. This paper proposes a unit-
modulus over-the-air computation (UMAirComp) framework to facilitate efficient edge
federated learning, which simultaneously uploads local model parameters and updates
global model parameters via analog beamforming. The proposed framework avoids
sophisticated baseband signal processing, leading to low communication delays and …