  Federated learning enables model training over a distributed corpus of agent
data. However, the trained model is vulnerable to adversarial examples,
designed to elicit misclassification. We study the feasibility of using
adversarial training (AT) in the federated learning setting. Furthermore, we do
so assuming a fixed communication budget and non-iid data distribution between
participating agents. We observe a significant drop in both natural and
adversarial accuracies when AT is used in the federated setting as opposed to
centralized training. We attribute this to the number of epochs of AT performed
locally at the agents, which in turn effects (i) drift between local models;
and (ii) convergence time (measured in number of communication rounds). Towards
this end, we propose FedDynAT, a novel algorithm for performing AT in federated
setting. Through extensive experimentation we show that FedDynAT significantly
improves both natural and adversarial accuracy, as well as model convergence
time by reducing the model drift.
