# Byzantine-Robust Aggregation for Securing Decentralized Federated Learning

arXiv: https://arxiv.org/abs/2409.17754 · HTML: https://mlsanity.com/paper/2409.17754/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 43 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Builds on (most cited first, 24 of 24)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [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)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping](https://mlsanity.com/paper/2012.13995/) (2020)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Attack of the Tails: Yes, You Really Can Backdoor Federated Learning](https://mlsanity.com/paper/2007.05084/) (2020)
- [A Hybrid Approach to Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1812.03224/) (2018)
- [Threats to Federated Learning: A Survey](https://mlsanity.com/paper/2003.02133/) (2020)
- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges](https://mlsanity.com/paper/2104.01776/) (2021)
- [When Machine Learning Meets Privacy: A Survey and Outlook](https://mlsanity.com/paper/2011.11819/) (2020)
- [Federated Learning of Gboard Language Models with Differential Privacy](https://mlsanity.com/paper/2305.18465/) (2023)
- [Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning](https://mlsanity.com/paper/2112.14468/) (2021)
- [Improving the Model Consistency of Decentralized Federated Learning](https://mlsanity.com/paper/2302.04083/) (2023)
- [DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation](https://mlsanity.com/paper/1907.12205/) (2019)
- [An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning](https://mlsanity.com/paper/2302.07173/) (2023)
- [COLA: Decentralized Linear Learning](https://mlsanity.com/paper/1808.04883/) (2018)
- [Byzantine-Tolerant Machine Learning](https://mlsanity.com/paper/1703.02757/) (2017)
- [On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing](https://mlsanity.com/paper/2303.10695/) (2023)
- [Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates](https://mlsanity.com/paper/2107.01477/) (2021)
- [Scheduling and Communication Schemes for Decentralized Federated Learning](https://mlsanity.com/paper/2311.16021/) (2023)

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

- [Screen Before You Fetch: Compressed Byzantine Screening for Decentralized Learning on the Edge-Cloud Continuum](https://mlsanity.com/paper/2510.07922/) (2025)
- [CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection](https://mlsanity.com/paper/2504.01882/) (2025)
- [Realistic Urban Traffic Generator using Decentralized Federated Learning for the SUMO simulator](https://mlsanity.com/paper/2506.07980/) (2025)
- [FedBGS: A Blockchain Approach to Segment Gossip Learning in Decentralized Systems](https://mlsanity.com/paper/2602.01185/) (2026)
- [Trustworthy Blockchain-based Federated Learning for Electronic Health Records: Securing Participant Identity with Decentralized Identifiers and Verifiable Credentials](https://mlsanity.com/paper/2602.02629/) (2026)
- [Decentralized Orchestration Architecture for Fluid Computing: A Secure Distributed AI Use Case](https://mlsanity.com/paper/2603.12001/) (2026)
- [Byzantine Accountability Without Consensus: Strong Eventual Consistency for Non-Associative, Stochastic, Robust Aggregation](https://mlsanity.com/paper/2607.10305/) (2026)

## Abstract (arXiv)

Federated Learning (FL) emerges as a distributed machine learning approach that addresses privacy concerns by training AI models locally on devices. Decentralized Federated Learning (DFL) extends the FL paradigm by eliminating the central server, thereby enhancing scalability and robustness through the avoidance of a single point of failure. However, DFL faces significant challenges in optimizing security, as most Byzantine-robust algorithms proposed in the literature are designed for centralized scenarios. In this paper, we present a novel Byzantine-robust aggregation algorithm to enhance the security of Decentralized Federated Learning environments, coined WFAgg. This proposal handles adverse conditions and strengthens the robustness of dynamic decentralized topologies at the same time by employing multiple filters to identify and mitigate Byzantine attacks. Experimental results demonstrate the effectiveness of the proposed algorithm in maintaining model accuracy and convergence in the presence of various Byzantine attack scenarios, outperforming state-of-the-art centralized Byzantine-robust aggregation schemes (such as Multi-Krum or Clustering). These algorithms are evaluated on an IID image classification problem in both centralized and decentralized scenarios.