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采用。这些框架利用去中心化环境的优势，支持设备间快速且节能的通信。然而，这种日益普及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安全的诸多方面，但在去中心化网络中对抗性节点放置的作用仍很大程度上未被探索。本文通过分析各种对抗性放置策略下（当对手能够联合协调其在网络中的放置时）去中心化FL的性能，填补了这一研究空白。我们建立了两种放置对抗性节点的基线策略：随机放置和基于网络中心性的放置。在此基础上，我们提出了一种新颖的攻击算法，该算法优先考虑对抗性传播而非对抗性中心性，通过最大化对手之间的平均网络距离来实现。我们表明，新攻击算法显著影响测试准确率等关键性能指标，在考虑的配置下，其效果比基线框架高出$9\%$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，为未来旨在开发更安全、更稳健的去中心化FL框架的研究奠定了基础。