# Poisoning Attacks and Defenses in Federated Learning: A Survey

arXiv: https://arxiv.org/abs/2301.05795 · HTML: https://mlsanity.com/paper/2301.05795/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 15 references that are in the map.
- First posted on arXiv 14 Jan 2023.

## Builds on (most cited first, 13 of 13)

- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [Attack of the Tails: Yes, You Really Can Backdoor Federated Learning](https://mlsanity.com/paper/2007.05084/) (2020)
- [Certified Defenses for Data Poisoning Attacks](https://mlsanity.com/paper/1706.03691/) (2017)
- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [Can You Really Backdoor Federated Learning?](https://mlsanity.com/paper/1911.07963/) (2019)
- [Federated Optimization:Distributed Optimization Beyond the Datacenter](https://mlsanity.com/paper/1511.03575/) (2015)
- [FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients](https://mlsanity.com/paper/2207.09209/) (2022)
- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [BaFFLe: Backdoor detection via Feedback-based Federated Learning](https://mlsanity.com/paper/2011.02167/) (2020)
- [LoMar: A Local Defense Against Poisoning Attack on Federated Learning](https://mlsanity.com/paper/2201.02873/) (2022)
- [FLAME: Taming Backdoors in Federated Learning (Extended Version 1)](https://mlsanity.com/paper/2101.02281/) (2021)

## Built on by (most cited first, 3 of 3)

- [On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions](https://mlsanity.com/paper/2508.13730/) (2025)
- [Fed-Credit: Robust Federated Learning with Credibility Management](https://mlsanity.com/paper/2405.11758/) (2024)
- [PoiCGAN: A Targeted Poisoning Based on Feature-Label Joint Perturbation in Federated Learning](https://mlsanity.com/paper/2603.23574/) (2026)

## Abstract (arXiv)

Federated learning (FL) enables the training of models among distributed clients without compromising the privacy of training datasets, while the invisibility of clients datasets and the training process poses a variety of security threats. This survey provides the taxonomy of poisoning attacks and experimental evaluation to discuss the need for robust FL.