# Impact of Network Topology on Byzantine Resilience in Decentralized Federated Learning

arXiv: https://arxiv.org/abs/2407.05141 · HTML: https://mlsanity.com/paper/2407.05141/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 6 Jul 2024.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [A Survey on Distributed Machine Learning](https://mlsanity.com/paper/1912.09789/) (2019)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Federated Learning Enables Big Data for Rare Cancer Boundary Detection](https://mlsanity.com/paper/2204.10836/) (2022)
- [When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions](https://mlsanity.com/paper/2306.15546/) (2023)
- [Decentralized Federated Learning: A Segmented Gossip Approach](https://mlsanity.com/paper/1908.07782/) (2019)
- [Deep Federated Learning for Autonomous Driving](https://mlsanity.com/paper/2110.05754/) (2021)
- [Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning](https://mlsanity.com/paper/2112.14468/) (2021)
- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning](https://mlsanity.com/paper/2302.07173/) (2023)
- [Byzantine-Robust Decentralized Learning via ClippedGossip](https://mlsanity.com/paper/2202.01545/) (2022)
- [Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey](https://mlsanity.com/paper/2302.02573/) (2023)
- [Fedstellar: A Platform for Decentralized Federated Learning](https://mlsanity.com/paper/2306.09750/) (2023)
- [Impact of network topology on the performance of Decentralized Federated Learning](https://mlsanity.com/paper/2402.18606/) (2024)
- [Improved Connectivity Condition for Byzantine Fault Tolerance](https://mlsanity.com/paper/1503.02774/) (2015)

## Abstract (arXiv)

Federated learning (FL) enables a collaborative environment for training machine learning models without sharing training data between users. This is typically achieved by aggregating model gradients on a central server. Decentralized federated learning is a rising paradigm that enables users to collaboratively train machine learning models in a peer-to-peer manner, without the need for a central aggregation server. However, before applying decentralized FL in real-world use training environments, nodes that deviate from the FL process (Byzantine nodes) must be considered when selecting an aggregation function. Recent research has focused on Byzantine-robust aggregation for client-server or fully connected networks, but has not yet evaluated such aggregation schemes for complex topologies possible with decentralized FL. Thus, the need for empirical evidence of Byzantine robustness in differing network topologies is evident. This work investigates the effects of state-of-the-art Byzantine-robust aggregation methods in complex, large-scale network structures. We find that state-of-the-art Byzantine robust aggregation strategies are not resilient within large non-fully connected networks. As such, our findings point the field towards the development of topology-aware aggregation schemes, especially necessary within the context of large scale real-world deployment.