  Federated edge learning (FEEL) is a framework for training models in a
distributed fashion using edge devices and a server that coordinates the
learning process. In FEEL, edge devices periodically transmit model parameters
to the server, which aggregates them to generate a global model. To reduce the
burden of transmitting high-dimensional data by many edge devices, a broadband
analog transmission scheme has been proposed. The devices transmit the
parameters simultaneously using a linear analog modulation, which are
aggregated by the superposition nature of the wireless medium. However, linear
analog modulations incur in an excessive power consumption for edge devices and
are not suitable for current digital wireless systems. To overcome this issue,
in this paper we propose a digital frequency broadband aggregation. The scheme
integrates a Multiple Frequency Shift Keying (MFSK) at the transmitters and a
type-based multiple access (TBMA) at the receiver. Using concurrent
transmission, the server can recover the type (i.e., a histogram) of the
transmitted parameters and compute any aggregation function to generate a
shared global model. We provide an extensive analysis of the communication
scheme in an additive white Gaussian noise (AWGN) channel and compare it with
linear analog modulations. Our experimental results show that the proposed
scheme achieves no drop in performance up to $-10$ dB and outperforms the
analog counterparts, while requiring 14 dB less in peak-to-average power ratio
(PAPR) than linear analog modulations.
