# Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing

arXiv: https://arxiv.org/abs/2103.16031 · HTML: https://mlsanity.com/paper/2103.16031/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 17 references that are in the map.
- First posted on arXiv 30 Mar 2021.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Certified Adversarial Robustness via Randomized Smoothing](https://mlsanity.com/paper/1902.02918/) (2019)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Certified Robustness to Adversarial Examples with Differential Privacy](https://mlsanity.com/paper/1802.03471/) (2018)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers](https://mlsanity.com/paper/1906.04584/) (2019)
- [A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning](https://mlsanity.com/paper/2006.06224/) (2020)
- [FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling](https://mlsanity.com/paper/2009.10748/) (2020)
- [Federated Learning in Adversarial Settings](https://mlsanity.com/paper/2010.07808/) (2020)

## Built on by (most cited first, 9 of 9)

- [Delving into the Adversarial Robustness of Federated Learning](https://mlsanity.com/paper/2302.09479/) (2023)
- [Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges](https://mlsanity.com/paper/2407.06754/) (2024)
- [Certified Robustness in Federated Learning](https://mlsanity.com/paper/2206.02535/) (2022)
- [Characterizing Internal Evasion Attacks in Federated Learning](https://mlsanity.com/paper/2209.08412/) (2022)
- [Improving Machine Learning Robustness via Adversarial Training](https://mlsanity.com/paper/2309.12593/) (2023)
- [Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning](https://mlsanity.com/paper/2310.11594/) (2023)
- [On the Conflict of Robustness and Learning in Collaborative Machine Learning](https://mlsanity.com/paper/2402.13700/) (2024)
- [Federated Adversarial Training with Transformers](https://mlsanity.com/paper/2206.02131/) (2022)
- [FedCert: Federated Accuracy Certification](https://mlsanity.com/paper/2410.03067/) (2024)

## Abstract (arXiv)

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 ℓ2-bounded adversarial perturbations in a distributed setup. We also show that one-point gradient estimation based training approach is 2-3× faster than popular stochastic estimator based approach without any noticeable certified robustness differences.