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Wireless federated learning (FL) is an emerging machine learning paradigmthat trains a global parametric model from distributed datasets via wirelesscommunications. This paper proposes a unit-modulus wireless FL (UMWFL)framework, which simultaneously uploads local model parameters and computesglobal model parameters via optimized phase shifting. The proposed frameworkavoids sophisticated baseband signal processing, leading to both lowcommunication delays and implementation costs. A training loss bound is derivedand a penalty alternating minimization (PAM) algorithm is proposed to minimizethe nonconvex nonsmooth loss bound. Experimental results in the Car Learning toAct (CARLA) platform show that the proposed UMWFL framework with PAM algorithmachieves smaller training losses and testing errors than those of the benchmarkscheme.

Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization