# Learning to Detect Malicious Clients for Robust Federated Learning

arXiv: https://arxiv.org/abs/2002.00211 · HTML: https://mlsanity.com/paper/2002.00211/

## Key facts

- Cited by 88 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2020.

## Built on by (most cited first, 30 of 88)

- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients](https://mlsanity.com/paper/2207.09209/) (2022)
- [Neurotoxin: Durable Backdoors in Federated Learning](https://mlsanity.com/paper/2206.10341/) (2022)
- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection](https://mlsanity.com/paper/2201.00763/) (2022)
- [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)
- [FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning](https://mlsanity.com/paper/2210.12873/) (2022)
- [Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions](https://mlsanity.com/paper/2303.02213/) (2023)
- [LoMar: A Local Defense Against Poisoning Attack on Federated Learning](https://mlsanity.com/paper/2201.02873/) (2022)
- [Integration of Large Language Models and Federated Learning](https://mlsanity.com/paper/2307.08925/) (2023)
- [Decentralized Federated Learning: A Survey on Security and Privacy](https://mlsanity.com/paper/2401.17319/) (2024)
- [A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy](https://mlsanity.com/paper/2302.10637/) (2023)
- [BayBFed: Bayesian Backdoor Defense for Federated Learning](https://mlsanity.com/paper/2301.09508/) (2023)
- [Multi-metrics adaptively identifies backdoors in Federated learning](https://mlsanity.com/paper/2303.06601/) (2023)
- [Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey](https://mlsanity.com/paper/2312.08667/) (2023)
- [Certified Robustness to Data Poisoning in Gradient-Based Training](https://mlsanity.com/paper/2406.05670/) (2024)
- [Robust Federated Learning with Attack-Adaptive Aggregation](https://mlsanity.com/paper/2102.05257/) (2021)
- [A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee](https://mlsanity.com/paper/2108.00365/) (2021)
- [FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation](https://mlsanity.com/paper/2305.05506/) (2023)
- [FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning](https://mlsanity.com/paper/2106.02310/) (2021)
- [Defenses in Adversarial Machine Learning: A Survey](https://mlsanity.com/paper/2312.08890/) (2023)
- [Auto-weighted Robust Federated Learning with Corrupted Data Sources](https://mlsanity.com/paper/2101.05880/) (2021)
- [Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutation](https://mlsanity.com/paper/2204.03500/) (2022)
- [FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy](https://mlsanity.com/paper/2310.09789/) (2023)
- [Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates](https://mlsanity.com/paper/2107.01477/) (2021)
- [Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health](https://mlsanity.com/paper/2108.10761/) (2021)
- [Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning](https://mlsanity.com/paper/2208.10273/) (2022)
- [When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection](https://mlsanity.com/paper/2308.03573/) (2023)
- [Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2401.08216/) (2024)

## Abstract (arXiv)

Federated learning systems are vulnerable to attacks from malicious clients. As the central server in the system cannot govern the behaviors of the clients, a rogue client may initiate an attack by sending malicious model updates to the server, so as to degrade the learning performance or enforce targeted model poisoning attacks (a.k.a. backdoor attacks). Therefore, timely detecting these malicious model updates and the underlying attackers becomes critically important. In this work, we propose a new framework for robust federated learning where the central server learns to detect and remove the malicious model updates using a powerful detection model, leading to targeted defense. We evaluate our solution in both image classification and sentiment analysis tasks with a variety of machine learning models. Experimental results show that our solution ensures robust federated learning that is resilient to both the Byzantine attacks and the targeted model poisoning attacks.