Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Certifiably-Robust Federated Adversarial Learning via Randomized
  Smoothing   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.

23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess