  To further preserve model weight privacy and improve model performance in
Federated Learning (FL), FL via Over-the-Air Computation (AirComp) scheme based
on dynamic power control is proposed. The edge devices (EDs) transmit the signs
of local stochastic gradients by activating two adjacent orthogonal frequency
division multi-plexing (OFDM) subcarriers, and majority votes (MVs) at the edge
server (ES) are obtained by exploiting the energy accumulation on the
subcarriers. Then, we propose a dynamic power control algorithm to further
offset the biased aggregation of the MV aggregation values. We show that the
whole scheme can mitigate the impact of the time synchronization error, channel
fading and noise. The theoretical convergence proof of the scheme is
re-derived.

Machine Learning