  An oft-cited challenge of federated learning is the presence of
heterogeneity. \emph{Data heterogeneity} refers to the fact that data from
different clients may follow very different distributions. \emph{System
heterogeneity} refers to client devices having different system capabilities. A
considerable number of federated optimization methods address this challenge.
In the literature, empirical evaluations usually start federated training from
random initialization. However, in many practical applications of federated
learning, the server has access to proxy data for the training task that can be
used to pre-train a model before starting federated training. Using four
standard federated learning benchmark datasets, we empirically study the impact
of starting from a pre-trained model in federated learning. Unsurprisingly,
starting from a pre-trained model reduces the training time required to reach a
target error rate and enables the training of more accurate models (up to 40\%)
than is possible when starting from random initialization. Surprisingly, we
also find that starting federated learning from a pre-trained initialization
reduces the effect of both data and system heterogeneity. We recommend future
work proposing and evaluating federated optimization methods to evaluate the
performance when starting from random and pre-trained initializations. This
study raises several questions for further work on understanding the role of
heterogeneity in federated optimization. \footnote{Our code is available at:
\url{https://github.com/facebookresearch/where_to_begin}}
