  We compare communication efficiencies of two compelling distributed machine
learning approaches of split learning and federated learning. We show useful
settings under which each method outperforms the other in terms of
communication efficiency. We consider various practical scenarios of
distributed learning setup and juxtapose the two methods under various
real-life scenarios. We consider settings of small and large number of clients
as well as small models (1M - 6M parameters), large models (10M - 200M
parameters) and very large models (1 Billion-100 Billion parameters). We show
that increasing number of clients or increasing model size favors split
learning setup over the federated while increasing the number of data samples
while keeping the number of clients or model size low makes federated learning
more communication efficient.
