  Federated edge learning (FEEL) has emerged as a core paradigm for large-scale
optimization. However, FEEL still suffers from a communication bottleneck due
to the transmission of high-dimensional model updates from the clients to the
federator. Over-the-air computation (AirComp) leverages the additive property
of multiple-access channels by aggregating the clients' updates over the
channel to save communication resources. While analog uncoded transmission can
benefit from the increased signal-to-noise ratio (SNR) due to the simultaneous
transmission of many clients, potential errors may severely harm the learning
process for small SNRs. To alleviate this problem, channel coding approaches
were recently proposed for AirComp in FEEL. However, their error-correction
capability degrades with an increasing number of clients. We propose a digital
lattice-based code construction with constant error-correction capabilities in
the number of clients, and compare to nested-lattice codes, well-known for
their optimal rate and power efficiency in the point-to-point AWGN channel.
