# FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients

arXiv: https://arxiv.org/abs/2207.09209 · HTML: https://mlsanity.com/paper/2207.09209/

## Key facts

- Cited by 125 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 19 Jul 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping](https://mlsanity.com/paper/2012.13995/) (2020)
- [A Little Is Enough: Circumventing Defenses For Distributed Learning](https://mlsanity.com/paper/1902.06156/) (2019)
- [Learning to Detect Malicious Clients for Robust Federated Learning](https://mlsanity.com/paper/2002.00211/) (2020)
- [MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients](https://mlsanity.com/paper/2203.08669/) (2022)
- [Provably Secure Federated Learning against Malicious Clients](https://mlsanity.com/paper/2102.01854/) (2021)

## Built on by (most cited first, 30 of 125)

- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [A Survey on Federated Unlearning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2310.20448/) (2023)
- [FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information](https://mlsanity.com/paper/2210.10936/) (2022)
- [Byzantine-Robust Decentralized Federated Learning](https://mlsanity.com/paper/2406.10416/) (2024)
- [Backdoor Defense via Deconfounded Representation Learning](https://mlsanity.com/paper/2303.06818/) (2023)
- [Eliminating Domain Bias for Federated Learning in Representation Space](https://mlsanity.com/paper/2311.14975/) (2023)
- [Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions](https://mlsanity.com/paper/2303.02213/) (2023)
- [FLCert: Provably Secure Federated Learning against Poisoning Attacks](https://mlsanity.com/paper/2210.00584/) (2022)
- [AFLGuard: Byzantine-robust Asynchronous Federated Learning](https://mlsanity.com/paper/2212.06325/) (2022)
- [A Survey on Decentralized Federated Learning](https://mlsanity.com/paper/2308.04604/) (2023)
- [Learning Subpocket Prototypes for Generalizable Structure-based Drug Design](https://mlsanity.com/paper/2305.13997/) (2023)
- [A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy](https://mlsanity.com/paper/2302.10637/) (2023)
- [Backdoor Federated Learning by Poisoning Backdoor-Critical Layers](https://mlsanity.com/paper/2308.04466/) (2023)
- [FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation](https://mlsanity.com/paper/2305.05506/) (2023)
- [FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks](https://mlsanity.com/paper/2308.05832/) (2023)
- [Robustness and Cybersecurity in the EU Artificial Intelligence Act](https://mlsanity.com/paper/2502.16184/) (2025)
- [Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning](https://mlsanity.com/paper/2304.10783/) (2023)
- [FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy](https://mlsanity.com/paper/2310.09789/) (2023)
- [G2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering](https://mlsanity.com/paper/2306.04984/) (2023)
- [Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach](https://mlsanity.com/paper/2311.18498/) (2023)
- [ScionFL: Efficient and Robust Secure Quantized Aggregation](https://mlsanity.com/paper/2210.07376/) (2022)
- [FLTracer: Accurate Poisoning Attack Provenance in Federated Learning](https://mlsanity.com/paper/2310.13424/) (2023)
- [Provably Robust Federated Reinforcement Learning](https://mlsanity.com/paper/2502.08123/) (2025)
- [Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2401.08216/) (2024)
- [MARS: A Malignity-Aware Backdoor Defense in Federated Learning](https://mlsanity.com/paper/2509.20383/) (2025)
- [Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective](https://mlsanity.com/paper/2309.16456/) (2023)
- [Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning](https://mlsanity.com/paper/2407.07221/) (2024)
- [Vertical Federated Unlearning via Backdoor Certification](https://mlsanity.com/paper/2412.11476/) (2024)
- [FilterFL: Knowledge Filtering-based Data-Free Backdoor Defense for Federated Learning](https://mlsanity.com/paper/2308.11333/) (2023)
- [AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification](https://mlsanity.com/paper/2311.06996/) (2023)

## Abstract (arXiv)

Federated learning (FL) is vulnerable to model poisoning attacks, in which malicious clients corrupt the global model via sending manipulated model updates to the server. Existing defenses mainly rely on Byzantine-robust FL methods, which aim to learn an accurate global model even if some clients are malicious. However, they can only resist a small number of malicious clients in practice. It is still an open challenge how to defend against model poisoning attacks with a large number of malicious clients. Our FLDetector addresses this challenge via detecting malicious clients. FLDetector aims to detect and remove the majority of the malicious clients such that a Byzantine-robust FL method can learn an accurate global model using the remaining clients. Our key observation is that, in model poisoning attacks, the model updates from a client in multiple iterations are inconsistent. Therefore, FLDetector detects malicious clients via checking their model-updates consistency. Roughly speaking, the server predicts a client's model update in each iteration based on its historical model updates using the Cauchy mean value theorem and L-BFGS, and flags a client as malicious if the received model update from the client and the predicted model update are inconsistent in multiple iterations. Our extensive experiments on three benchmark datasets show that FLDetector can accurately detect malicious clients in multiple state-of-the-art model poisoning attacks. After removing the detected malicious clients, existing Byzantine-robust FL methods can learn accurate global models.Our code is available at https://github.com/zaixizhang/FLDetector.