  Federated learning is an emerging data-private distributed learning
framework, which, however, is vulnerable to adversarial attacks. Although
several heuristic defenses are proposed to enhance the robustness of federated
learning, they do not provide certifiable robustness guarantees. In this paper,
we incorporate randomized smoothing techniques into federated adversarial
training to enable data-private distributed learning with certifiable
robustness to test-time adversarial perturbations. Our experiments show that
such an advanced federated adversarial learning framework can deliver models as
robust as those trained by the centralized training. Further, this enables
provably-robust classifiers to $\ell_2$-bounded adversarial perturbations in a
distributed setup. We also show that one-point gradient estimation based
training approach is $2-3\times$ faster than popular stochastic estimator based
approach without any noticeable certified robustness differences.
