# Dynamic Defense Against Byzantine Poisoning Attacks in Federated Learning

arXiv: https://arxiv.org/abs/2007.15030 · HTML: https://mlsanity.com/paper/2007.15030/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [A Snapshot of the Frontiers of Client Selection in Federated Learning](https://mlsanity.com/paper/2210.04607/) (2022)
- [Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences](https://mlsanity.com/paper/2311.14127/) (2023)
- [Federated Learning Based Multilingual Emoji Prediction In Clean and Attack Scenarios](https://mlsanity.com/paper/2304.01005/) (2023)
- [FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selection](https://mlsanity.com/paper/2504.11216/) (2025)
- [Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions](https://mlsanity.com/paper/2603.23472/) (2026)
- [Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things](https://mlsanity.com/paper/2310.01537/) (2023)
- [RAB2-DEF: Dynamic and explainable defense against adversarial attacks in Federated Learning to fair poor clients](https://mlsanity.com/paper/2410.08244/) (2024)
- [Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization](https://mlsanity.com/paper/2608.06563/) (2026)

## Abstract (arXiv)

Federated learning, as a distributed learning that conducts the training on the local devices without accessing to the training data, is vulnerable to Byzatine poisoning adversarial attacks. We argue that the federated learning model has to avoid those kind of adversarial attacks through filtering out the adversarial clients by means of the federated aggregation operator. We propose a dynamic federated aggregation operator that dynamically discards those adversarial clients and allows to prevent the corruption of the global learning model. We assess it as a defense against adversarial attacks deploying a deep learning classification model in a federated learning setting on the Fed-EMNIST Digits, Fashion MNIST and CIFAR-10 image datasets. The results show that the dynamic selection of the clients to aggregate enhances the performance of the global learning model and discards the adversarial and poor (with low quality models) clients.