# The Impact of Adversarial Node Placement in Decentralized Federated Learning Networks

arXiv: https://arxiv.org/abs/2311.07946 · HTML: https://mlsanity.com/paper/2311.07946/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 11 of 11)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Adversarial Attacks on Neural Network Policies](https://mlsanity.com/paper/1702.02284/) (2017)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Threats to Federated Learning: A Survey](https://mlsanity.com/paper/2003.02133/) (2020)
- [Provably Secure Federated Learning against Malicious Clients](https://mlsanity.com/paper/2102.01854/) (2021)
- [Distributed stochastic optimization with gradient tracking over strongly-connected networks](https://mlsanity.com/paper/1903.07266/) (2019)
- [Backdoor Attacks in Peer-to-Peer Federated Learning](https://mlsanity.com/paper/2301.09732/) (2023)
- [Can Decentralized Learning be more robust than Federated Learning?](https://mlsanity.com/paper/2303.03829/) (2023)
- [How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?](https://mlsanity.com/paper/2301.08866/) (2023)

## Built on by (most cited first, 1 of 1)

- [On Balancing Sparsity with Reliable Connectivity in Distributed Network Design with Random K-out Graphs](https://mlsanity.com/paper/2508.11863/) (2025)

## Abstract (arXiv)

As Federated Learning (FL) grows in popularity, new decentralized frameworks are becoming widespread. These frameworks leverage the benefits of decentralized environments to enable fast and energy-efficient inter-device communication. However, this growing popularity also intensifies the need for robust security measures. While existing research has explored various aspects of FL security, the role of adversarial node placement in decentralized networks remains largely unexplored. This paper addresses this gap by analyzing the performance of decentralized FL for various adversarial placement strategies when adversaries can jointly coordinate their placement within a network. We establish two baseline strategies for placing adversarial node: random placement and network centrality-based placement. Building on this foundation, we propose a novel attack algorithm that prioritizes adversarial spread over adversarial centrality by maximizing the average network distance between adversaries. We show that the new attack algorithm significantly impacts key performance metrics such as testing accuracy, outperforming the baseline frameworks by between 9% and 66.5% for the considered setups. Our findings provide valuable insights into the vulnerabilities of decentralized FL systems, setting the stage for future research aimed at developing more secure and robust decentralized FL frameworks.