  Federated Learning (FL) is a distributed machine learning paradigm where data
is distributed among clients who collaboratively train a model in a computation
process coordinated by a central server. By assigning a weight to each client
based on the proportion of data instances it possesses, the rate of convergence
to an accurate joint model can be greatly accelerated. Some previous works
studied FL in a Byzantine setting, in which a fraction of the clients may send
arbitrary or even malicious information regarding their model. However, these
works either ignore the issue of data unbalancedness altogether or assume that
client weights are apriori known to the server, whereas, in practice, it is
likely that weights will be reported to the server by the clients themselves
and therefore cannot be relied upon. We address this issue for the first time
by proposing a practical weight-truncation-based preprocessing method and
demonstrating empirically that it is able to strike a good balance between
model quality and Byzantine robustness. We also establish analytically that our
method can be applied to a randomly selected sample of client weights.
