  In the context of task-oriented communications we advocate the development of
waveforms for Federated Edge Learning (FEEL). Over-the-air computing (AirComp)
has emerged as a communication scheme that allows to compute a function out of
distributed data and can be applied to FEEL. However, the design of modulations
for AirComp is still in its infancy and most of the literature ignores this
topic. In this work we employ frequency modulation (FM) and type based multiple
access (TMBA) for FEEL and demonstrate its advantages with respect to the state
of the art in terms of convergence and peak-to-average power ratio (PAPR).
