Gradient dataset aware configuration for over-the-air (ota) model aggregation in federated learning

Abstract

A method performed by a user equipment (UE) generates local gradients for a federated learning task. The method calculates a gradient Sum-Power level based on the local gradients. The method receives a mapping between gradient Sum-Power levels and scaling factors for channel inversion coefficients to process a data block into an unencoded uplink signal. The method also determines a channel inversion coefficient based on a scaling factor obtained from the mapping and the calculated gradient Sum-Power level. The method applies analog modulation and the channel inversion coefficient to the data block to form the unencoded uplink signal. The method further transmits, to a network, the unencoded uplink signal on shared uplink resources for an over-the-air computation of global gradients for the federated learning task.