# Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning

arXiv: https://arxiv.org/abs/2112.14468 · HTML: https://mlsanity.com/paper/2112.14468/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 29 Dec 2021.

## Builds on (most cited first, 14 of 14)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping](https://mlsanity.com/paper/2012.13995/) (2020)
- [The Hidden Vulnerability of Distributed Learning in Byzantium](https://mlsanity.com/paper/1802.07927/) (2018)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Mitigating Sybils in Federated Learning Poisoning](https://mlsanity.com/paper/1808.04866/) (2018)
- [RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets](https://mlsanity.com/paper/1811.03761/) (2018)
- [Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning](https://mlsanity.com/paper/2006.12097/) (2020)
- [Generalized Byzantine-tolerant SGD](https://mlsanity.com/paper/1802.10116/) (2018)
- [Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging](https://mlsanity.com/paper/1909.05125/) (2019)
- [Abnormal Client Behavior Detection in Federated Learning](https://mlsanity.com/paper/1910.09933/) (2019)
- [FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning](https://mlsanity.com/paper/2109.05612/) (2021)

## Built on by (most cited first, 30 of 47)

- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning](https://mlsanity.com/paper/2302.07173/) (2023)
- [Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions](https://mlsanity.com/paper/2307.00974/) (2023)
- [RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation](https://mlsanity.com/paper/2405.15182/) (2024)
- [A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective](https://mlsanity.com/paper/2311.16065/) (2023)
- [When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection](https://mlsanity.com/paper/2308.03573/) (2023)
- [Byzantine-Robust Aggregation for Securing Decentralized Federated Learning](https://mlsanity.com/paper/2409.17754/) (2024)
- [MARS: A Malignity-Aware Backdoor Defense in Federated Learning](https://mlsanity.com/paper/2509.20383/) (2025)
- [AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification](https://mlsanity.com/paper/2311.06996/) (2023)
- [ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning](https://mlsanity.com/paper/2405.20975/) (2024)
- [Federated learning with incremental clustering for heterogeneous data](https://mlsanity.com/paper/2206.08752/) (2022)
- [Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications](https://mlsanity.com/paper/2207.08486/) (2022)
- [Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning](https://mlsanity.com/paper/2405.15632/) (2024)
- [Edge Security: Challenges and Issues](https://mlsanity.com/paper/2206.07164/) (2022)
- [Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection](https://mlsanity.com/paper/2303.16668/) (2023)
- [MISA: Unveiling the Vulnerabilities in Split Federated Learning](https://mlsanity.com/paper/2312.11026/) (2023)
- [FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2402.10082/) (2024)
- [A Framework for Incentivized Collaborative Learning](https://mlsanity.com/paper/2305.17052/) (2023)
- [FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks](https://mlsanity.com/paper/2312.04587/) (2023)
- [Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distance](https://mlsanity.com/paper/2503.21244/) (2025)
- [Can We Trust the Similarity Measurement in Federated Learning?](https://mlsanity.com/paper/2311.03369/) (2023)
- [Mitigating Malicious Attacks in Federated Learning via Confidence-aware Defense](https://mlsanity.com/paper/2408.02813/) (2024)
- [Federated Learning Nodes Can Reconstruct Peers' Image Data](https://mlsanity.com/paper/2410.04661/) (2024)
- [DistDD: Distributed Data Distillation Aggregation through Gradient Matching](https://mlsanity.com/paper/2410.08665/) (2024)
- [Byzantine-Robust Federated Learning: An Overview With Focus on Developing Sybil-based Attacks to Backdoor Augmented Secure Aggregation Protocols](https://mlsanity.com/paper/2410.22680/) (2024)
- [Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture](https://mlsanity.com/paper/2503.18284/) (2025)
- [Approximate Agreement Algorithms for Byzantine Collaborative Learning](https://mlsanity.com/paper/2504.01504/) (2025)
- [Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things](https://mlsanity.com/paper/2505.23792/) (2025)
- [On the Fragility of Contribution Score Computation in Federated Learning](https://mlsanity.com/paper/2509.19921/) (2025)
- [Fed-Credit: Robust Federated Learning with Credibility Management](https://mlsanity.com/paper/2405.11758/) (2024)

## Abstract (arXiv)

Recently emerged federated learning (FL) is an attractive distributed learning framework in which numerous wireless end-user devices can train a global model with the data remained autochthonous. Compared with the traditional machine learning framework that collects user data for centralized storage, which brings huge communication burden and concerns about data privacy, this approach can not only save the network bandwidth but also protect the data privacy. Despite the promising prospect, byzantine attack, an intractable threat in conventional distributed network, is discovered to be rather efficacious against FL as well. In this paper, we conduct a comprehensive investigation of the state-of-the-art strategies for defending against byzantine attacks in FL. We first provide a taxonomy for the existing defense solutions according to the techniques they used, followed by an across-the-board comparison and discussion. Then we propose a new byzantine attack method called weight attack to defeat those defense schemes, and conduct experiments to demonstrate its threat. The results show that existing defense solutions, although abundant, are still far from fully protecting FL. Finally, we indicate possible countermeasures for weight attack, and highlight several challenges and future research directions for mitigating byzantine attacks in FL.