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To further preserve model weight privacy and improve model performance inFederated Learning (FL), FL via Over-the-Air Computation (AirComp) scheme basedon dynamic power control is proposed. The edge devices (EDs) transmit the signsof local stochastic gradients by activating two adjacent orthogonal frequencydivision multi-plexing (OFDM) subcarriers, and majority votes (MVs) at the edgeserver (ES) are obtained by exploiting the energy accumulation on thesubcarriers. Then, we propose a dynamic power control algorithm to furtheroffset the biased aggregation of the MV aggregation values. We show that thewhole scheme can mitigate the impact of the time synchronization error, channelfading and noise. The theoretical convergence proof of the scheme isre-derived.

Private Federated Learning with Dynamic Power Control via Non-Coherent Over-the-Air Computation