# Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging

arXiv: https://arxiv.org/abs/1909.05125 · HTML: https://mlsanity.com/paper/1909.05125/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

## Built on by (most cited first, 30 of 70)

- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [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)
- [From Distributed Machine Learning to Federated Learning: A Survey](https://mlsanity.com/paper/2104.14362/) (2021)
- [Byzantine-Robust Decentralized Federated Learning](https://mlsanity.com/paper/2406.10416/) (2024)
- [Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning](https://mlsanity.com/paper/2112.14468/) (2021)
- [SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification](https://mlsanity.com/paper/2112.06274/) (2021)
- [FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective](https://mlsanity.com/paper/2110.13864/) (2021)
- [Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions](https://mlsanity.com/paper/2303.02213/) (2023)
- [Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering](https://mlsanity.com/paper/2109.05872/) (2021)
- [AFLGuard: Byzantine-robust Asynchronous Federated Learning](https://mlsanity.com/paper/2212.06325/) (2022)
- [CrowdGuard: Federated Backdoor Detection in Federated Learning](https://mlsanity.com/paper/2210.07714/) (2022)
- [FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2312.04432/) (2023)
- [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)
- [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)
- [Defending against the Label-flipping Attack in Federated Learning](https://mlsanity.com/paper/2207.01982/) (2022)
- [FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks](https://mlsanity.com/paper/2308.05832/) (2023)
- [Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges](https://mlsanity.com/paper/2407.06754/) (2024)
- [Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation](https://mlsanity.com/paper/2409.01435/) (2024)
- [Do We Really Need to Design New Byzantine-robust Aggregation Rules?](https://mlsanity.com/paper/2501.17381/) (2025)
- [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)
- [FedRAD: Federated Robust Adaptive Distillation](https://mlsanity.com/paper/2112.01405/) (2021)
- [Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks](https://mlsanity.com/paper/2403.03149/) (2024)
- [Data-Free Evaluation of User Contributions in Federated Learning](https://mlsanity.com/paper/2108.10623/) (2021)
- [Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning](https://mlsanity.com/paper/2304.10783/) (2023)
- [Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design](https://mlsanity.com/paper/2105.06256/) (2021)
- [RobustFed: A Truth Inference Approach for Robust Federated Learning](https://mlsanity.com/paper/2107.08402/) (2021)
- [Byzantine Fault-Tolerance in Federated Local SGD under 2f-Redundancy](https://mlsanity.com/paper/2108.11769/) (2021)

## Abstract (arXiv)

Federated learning enables training collaborative machine learning models at scale with many participants whilst preserving the privacy of their datasets. Standard federated learning techniques are vulnerable to Byzantine failures, biased local datasets, and poisoning attacks. In this paper we introduce Adaptive Federated Averaging, a novel algorithm for robust federated learning that is designed to detect failures, attacks, and bad updates provided by participants in a collaborative model. We propose a Hidden Markov Model to model and learn the quality of model updates provided by each participant during training. In contrast to existing robust federated learning schemes, we propose a robust aggregation rule that detects and discards bad or malicious local model updates at each training iteration. This includes a mechanism that blocks unwanted participants, which also increases the computational and communication efficiency. Our experimental evaluation on 4 real datasets show that our algorithm is significantly more robust to faulty, noisy and malicious participants, whilst being computationally more efficient than other state-of-the-art robust federated learning methods such as Multi-KRUM and coordinate-wise median.