# FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications

arXiv: https://arxiv.org/abs/2006.15632 · HTML: https://mlsanity.com/paper/2006.15632/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2203.09702/) (2022)
- [Fusion of Federated Learning and Industrial Internet of Things: A Survey](https://mlsanity.com/paper/2101.00798/) (2021)
- [Eliminating Backdoor Triggers for Deep Neural Networks Using Attention Relation Graph Distillation](https://mlsanity.com/paper/2204.09975/) (2022)
- [The Cost of Training Machine Learning Models over Distributed Data Sources](https://mlsanity.com/paper/2209.07124/) (2022)
- [Federated Learning for Industrial Internet of Things in Future Industries](https://mlsanity.com/paper/2105.14659/) (2021)
- [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024)
- [Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition](https://mlsanity.com/paper/2203.04696/) (2022)
- [Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications](https://mlsanity.com/paper/2310.05269/) (2023)
- [Model-Contrastive Learning for Backdoor Defense](https://mlsanity.com/paper/2205.04411/) (2022)
- [FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutation](https://mlsanity.com/paper/2411.15847/) (2024)

## Abstract (arXiv)

Along with the proliferation of Artificial Intelligence (AI) and Internet of Things (IoT) techniques, various kinds of adversarial attacks are increasingly emerging to fool Deep Neural Networks (DNNs) used by Industrial IoT (IIoT) applications. Due to biased training data or vulnerable underlying models, imperceptible modifications on inputs made by adversarial attacks may result in devastating consequences. Although existing methods are promising in defending such malicious attacks, most of them can only deal with limited existing attack types, which makes the deployment of large-scale IIoT devices a great challenge. To address this problem, we present an effective federated defense approach named FDA3 that can aggregate defense knowledge against adversarial examples from different sources. Inspired by federated learning, our proposed cloud-based architecture enables the sharing of defense capabilities against different attacks among IIoT devices. Comprehensive experimental results show that the generated DNNs by our approach can not only resist more malicious attacks than existing attack-specific adversarial training methods, but also can prevent IIoT applications from new attacks.