# Abnormal Client Behavior Detection in Federated Learning

arXiv: https://arxiv.org/abs/1910.09933 · HTML: https://mlsanity.com/paper/1910.09933/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

## Built on by (most cited first, 30 of 40)

- [DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection](https://mlsanity.com/paper/2201.00763/) (2022)
- [From Distributed Machine Learning to Federated Learning: A Survey](https://mlsanity.com/paper/2104.14362/) (2021)
- [FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusion](https://mlsanity.com/paper/2110.11027/) (2021)
- [Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning](https://mlsanity.com/paper/2112.14468/) (2021)
- [BayBFed: Bayesian Backdoor Defense for Federated Learning](https://mlsanity.com/paper/2301.09508/) (2023)
- [A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems](https://mlsanity.com/paper/2206.04149/) (2022)
- [Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking](https://mlsanity.com/paper/2205.13054/) (2022)
- [Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions](https://mlsanity.com/paper/2207.09611/) (2022)
- [Federated Continual Learning: Concepts, Challenges, and Solutions](https://mlsanity.com/paper/2502.07059/) (2025)
- [Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies](https://mlsanity.com/paper/2109.03999/) (2021)
- [Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health](https://mlsanity.com/paper/2108.10761/) (2021)
- [Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning](https://mlsanity.com/paper/2208.09894/) (2022)
- [ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning](https://mlsanity.com/paper/2405.20975/) (2024)
- [Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms](https://mlsanity.com/paper/2207.02337/) (2022)
- [Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications](https://mlsanity.com/paper/2207.08486/) (2022)
- [A Model Consistency-Based Countermeasure to GAN-Based Data Poisoning Attack in Federated Learning](https://mlsanity.com/paper/2405.11440/) (2024)
- [Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning](https://mlsanity.com/paper/2405.15632/) (2024)
- [Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach](https://mlsanity.com/paper/2104.13092/) (2021)
- [Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data](https://mlsanity.com/paper/2404.14933/) (2024)
- [Tackling Selfish Clients in Federated Learning](https://mlsanity.com/paper/2407.15402/) (2024)
- [FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data](https://mlsanity.com/paper/2408.04442/) (2024)
- [Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning](https://mlsanity.com/paper/2512.17254/) (2025)
- [DistFL: Distribution-aware Federated Learning for Mobile Scenarios](https://mlsanity.com/paper/2110.11619/) (2021)
- [Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression](https://mlsanity.com/paper/2409.04022/) (2024)
- [Stochastic Alternating Direction Method of Multipliers for Byzantine-Robust Distributed Learning](https://mlsanity.com/paper/2106.06891/) (2021)
- [Byzantine-Robust Federated Learning via Credibility Assessment on Non-IID Data](https://mlsanity.com/paper/2109.02396/) (2021)
- [Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective](https://mlsanity.com/paper/2302.08976/) (2023)
- [GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning](https://mlsanity.com/paper/2405.20727/) (2024)
- [Understanding Byzantine Robustness in Federated Learning with A Black-box Server](https://mlsanity.com/paper/2408.06042/) (2024)
- [CoopASD: Cooperative Machine Anomalous Sound Detection with Privacy Concerns](https://mlsanity.com/paper/2408.14753/) (2024)

## Abstract (arXiv)

In federated learning systems, clients are autonomous in that their behaviors are not fully governed by the server. Consequently, a client may intentionally or unintentionally deviate from the prescribed course of federated model training, resulting in abnormal behaviors, such as turning into a malicious attacker or a malfunctioning client. Timely detecting those anomalous clients is therefore critical to minimize their adverse impacts. In this work, we propose to detect anomalous clients at the server side. In particular, we generate low-dimensional surrogates of model weight vectors and use them to perform anomaly detection. We evaluate our solution through experiments on image classification model training over the FEMNIST dataset. Experimental results show that the proposed detection-based approach significantly outperforms the conventional defense-based methods.