  Spiking Neural Networks (SNNs) are biologically inspired alternatives to
conventional Artificial Neural Networks (ANNs). Despite promising preliminary
results, the trade-offs in the training of SNNs in a distributed scheme are not
well understood. Here, we consider SNNs in a federated learning setting where a
high-quality global model is created by aggregating multiple local models from
the clients without sharing any data. We investigate federated learning for
training multiple SNNs at clients when two mechanisms reduce the uplink
communication cost: i) random masking of the model updates sent from the
clients to the server; and ii) client dropouts where some clients do not send
their updates to the server. We evaluated the performance of the SNNs using a
subset of the Spiking Heidelberg digits (SHD) dataset. The results show that a
trade-off between the random masking and the client drop probabilities is
crucial to obtain a satisfactory performance for a fixed number of clients.
