Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing
This provides a solution for data-private distributed learning systems needing provable security against adversarial attacks, though it is incremental by building on existing techniques.
The paper tackles the vulnerability of federated learning to adversarial attacks by incorporating randomized smoothing into federated adversarial training, achieving certifiable robustness to test-time perturbations with models as robust as centralized training and a 2-3x faster training approach.
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.