# Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated Learning

arXiv: https://arxiv.org/abs/2208.10273 · HTML: https://mlsanity.com/paper/2208.10273/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 37 references that are in the map.
- First posted on arXiv 14 Aug 2022.

## Builds on (most cited first, 26 of 26)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [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)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives](https://mlsanity.com/paper/1407.0202/) (2014)
- [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)
- [Attack of the Tails: Yes, You Really Can Backdoor Federated Learning](https://mlsanity.com/paper/2007.05084/) (2020)
- [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)
- [RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets](https://mlsanity.com/paper/1811.03761/) (2018)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [FedVision: An Online Visual Object Detection Platform Powered by Federated Learning](https://mlsanity.com/paper/2001.06202/) (2020)
- [Generalized Byzantine-tolerant SGD](https://mlsanity.com/paper/1802.10116/) (2018)
- [Federated Learning for Keyword Spotting](https://mlsanity.com/paper/1810.05512/) (2018)
- [CRFL: Certifiably Robust Federated Learning against Backdoor Attacks](https://mlsanity.com/paper/2106.08283/) (2021)
- [Learning to Detect Malicious Clients for Robust Federated Learning](https://mlsanity.com/paper/2002.00211/) (2020)
- [Defending against Backdoors in Federated Learning with Robust Learning Rate](https://mlsanity.com/paper/2007.03767/) (2020)
- [Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks](https://mlsanity.com/paper/1912.12716/) (2019)
- [Provably Secure Federated Learning against Malicious Clients](https://mlsanity.com/paper/2102.01854/) (2021)
- [Game of Gradients: Mitigating Irrelevant Clients in Federated Learning](https://mlsanity.com/paper/2110.12257/) (2021)
- [New Convergence Aspects of Stochastic Gradient Algorithms](https://mlsanity.com/paper/1811.12403/) (2018)
- [Robust Federated Learning with Attack-Adaptive Aggregation](https://mlsanity.com/paper/2102.05257/) (2021)
- [Untargeted Poisoning Attack Detection in Federated Learning via Behavior Attestation](https://mlsanity.com/paper/2101.10904/) (2021)
- [Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design](https://mlsanity.com/paper/2105.06256/) (2021)

## Built on by (most cited first, 7 of 7)

- [Tackling Selfish Clients in Federated Learning](https://mlsanity.com/paper/2407.15402/) (2024)
- [Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning](https://mlsanity.com/paper/2506.09674/) (2025)
- [Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning](https://mlsanity.com/paper/2404.06230/) (2024)
- [Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning](https://mlsanity.com/paper/2501.04453/) (2025)
- [Can Quantum Federated Learning Withstand Circuit-Level Backdoors?](https://mlsanity.com/paper/2605.27416/) (2026)
- [When Clients Are Orchestrated: Strategic Gradient Manipulation to Defeat Federated Learning Servers with Efficient Defense](https://mlsanity.com/paper/2609.27124/) (2026)
- [Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunities](https://mlsanity.com/paper/2609.28722/) (2026)

## Abstract (arXiv)

Federated learning offers a framework of training a machine learning model in a distributed fashion while preserving privacy of the participants. As the server cannot govern the clients' actions, nefarious clients may attack the global model by sending malicious local gradients. In the meantime, there could also be unreliable clients who are benign but each has a portion of low-quality training data (e.g., blur or low-resolution images), thus may appearing similar as malicious clients. Therefore, a defense mechanism will need to perform a three-fold differentiation which is much more challenging than the conventional (two-fold) case. This paper introduces MUD-HoG, a novel defense algorithm that addresses this challenge in federated learning using long-short history of gradients, and treats the detected malicious and unreliable clients differently. Not only this, but we can also distinguish between targeted and untargeted attacks among malicious clients, unlike most prior works which only consider one type of the attacks. Specifically, we take into account sign-flipping, additive-noise, label-flipping, and multi-label-flipping attacks, under a non-IID setting. We evaluate MUD-HoG with six state-of-the-art methods on two datasets. The results show that MUD-HoG outperforms all of them in terms of accuracy as well as precision and recall, in the presence of a mixture of multiple (four) types of attackers as well as unreliable clients. Moreover, unlike most prior works which can only tolerate a low population of harmful users, MUD-HoG can work with and successfully detect a wide range of malicious and unreliable clients - up to 47.5% and 10%, respectively, of the total population. Our code is open-sourced at https://github.com/LabSAINT/MUD-HoG_Federated_Learning.