  Federated learning is a popular strategy for training models on distributed,
sensitive data, while preserving data privacy. Prior work identified a range of
security threats on federated learning protocols that poison the data or the
model. However, federated learning is a networked system where the
communication between clients and server plays a critical role for the learning
task performance. We highlight how communication introduces another
vulnerability surface in federated learning and study the impact of
network-level adversaries on training federated learning models. We show that
attackers dropping the network traffic from carefully selected clients can
significantly decrease model accuracy on a target population. Moreover, we show
that a coordinated poisoning campaign from a few clients can amplify the
dropping attacks. Finally, we develop a server-side defense which mitigates the
impact of our attacks by identifying and up-sampling clients likely to
positively contribute towards target accuracy. We comprehensively evaluate our
attacks and defenses on three datasets, assuming encrypted communication
channels and attackers with partial visibility of the network.
