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.

翻译：随着联邦学习（FL）日益普及，新型去中心化框架正变得普遍。这些框架利用去中心化环境的优势，实现设备间快速且节能的通信。然而，这种普及度的提升也加剧了对强健安全措施的需求。尽管现有研究已探索联邦学习安全的多个方面，但敌对节点在去中心化网络中的部署作用仍很大程度上未被挖掘。本文通过分析多种敌对部署策略下（当攻击者可协同在网络中部署节点时）去中心化联邦学习的性能，填补了这一空白。我们建立了两种敌对节点部署基线策略：随机部署和基于网络中心性的部署。在此基础之上，我们提出了一种新颖的攻击算法，该算法通过最大化攻击者之间的平均网络距离，优先考虑攻击者扩散而非攻击者中心性。我们证明，新攻击算法显著影响了测试准确率等关键性能指标，在所考虑的配置下相比基线框架提升了9%至66.5%。我们的研究结果揭示了出去中心化联邦学习系统的脆弱性，为未来旨在开发更安全、更强健的去中心化联邦学习框架的研究奠定了基础。