  Proposed as a solution to mitigate the privacy implications related to the
adoption of deep learning, Federated Learning (FL) enables large numbers of
participants to successfully train deep neural networks without having to
reveal the actual private training data. To date, a substantial amount of
research has investigated the security and privacy properties of FL, resulting
in a plethora of innovative attack and defense strategies. This paper
thoroughly investigates the communication capabilities of an FL scheme. In
particular, we show that a party involved in the FL learning process can use FL
as a covert communication medium to send an arbitrary message. We introduce
FedComm, a novel multi-system covert-communication technique that enables
robust sharing and transfer of targeted payloads within the FL framework. Our
extensive theoretical and empirical evaluations show that FedComm provides a
stealthy communication channel, with minimal disruptions to the training
process. Our experiments show that FedComm successfully delivers 100% of a
payload in the order of kilobits before the FL procedure converges. Our
evaluation also shows that FedComm is independent of the application domain and
the neural network architecture used by the underlying FL scheme.
