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，新的去中心化框架正变得广泛。这些框架利用去中心化环境的优势，实现设备间快速且能效优化的通信。然而，这一日益普及的趋势也加剧了对稳健安全措施的需求。尽管现有研究已探索了FL安全的多个方面，但去中心化网络中对抗性节点部署的作用仍鲜有涉及。本文通过分析当攻击者可在网络内协调部署位置时，不同对抗性部署策略对去中心化FL性能的影响，填补了这一研究空白。我们确立了两种基线策略来部署对抗性节点：随机部署和基于网络中心性的部署。在此基础上，我们提出了一种新型攻击算法，该算法通过最大化攻击者之间的平均网络距离，优先考虑对抗性节点的扩散而非中心性。研究表明，新攻击算法显著影响了测试准确率等关键性能指标，在所选设置下比基线框架的性能高出9%至66.5%。我们的发现为去中心化FL系统的脆弱性提供了宝贵见解，为未来开发更安全、更稳健的去中心化FL框架奠定了基础。