Federated Learning (FL) has emerged as a promising approach to address privacy concerns inherent in Machine Learning (ML) practices. However, conventional FL methods, particularly those following the Centralized FL (CFL) paradigm, utilize a central server for global aggregation, which exhibits limitations such as bottleneck and single point of failure. To address these issues, the Decentralized FL (DFL) paradigm has been proposed, which removes the client-server boundary and enables all participants to engage in model training and aggregation tasks. Nevertheless, as CFL, DFL remains vulnerable to adversarial attacks, notably poisoning attacks that undermine model performance. While existing research on model robustness has predominantly focused on CFL, there is a noteworthy gap in understanding the model robustness of the DFL paradigm. In this paper, a thorough review of poisoning attacks targeting the model robustness in DFL systems, as well as their corresponding countermeasures, are presented. Additionally, a solution called DART is proposed to evaluate the robustness of DFL models, which is implemented and integrated into a DFL platform. Through extensive experiments, this paper compares the behavior of CFL and DFL under diverse poisoning attacks, pinpointing key factors affecting attack spread and effectiveness within the DFL. It also evaluates the performance of different defense mechanisms and investigates whether defense mechanisms designed for CFL are compatible with DFL. The empirical results provide insights into research challenges and suggest ways to improve the robustness of DFL models for future research.

翻译：联邦学习（FL）已成为解决机器学习（ML）实践中固有隐私问题的一种有前景的方法。然而，传统的FL方法，特别是那些遵循中心化联邦学习（CFL）范式的方法，利用中央服务器进行全局聚合，这存在诸如瓶颈和单点故障等局限性。为解决这些问题，去中心化联邦学习（DFL）范式被提出，它移除了客户端-服务器边界，使所有参与者都能参与模型训练和聚合任务。尽管如此，与CFL一样，DFL仍然容易受到对抗性攻击，特别是那些损害模型性能的投毒攻击。虽然现有的模型鲁棒性研究主要集中在CFL上，但对DFL范式模型鲁棒性的理解存在显著差距。本文全面综述了针对DFL系统模型鲁棒性的投毒攻击及其相应的防御对策。此外，本文提出了一种名为DART的解决方案，用于评估DFL模型的鲁棒性，该方案已实现并集成到一个DFL平台中。通过大量实验，本文比较了CFL和DFL在不同投毒攻击下的行为，确定了影响攻击在DFL内传播和有效性的关键因素。本文还评估了不同防御机制的性能，并探讨了为CFL设计的防御机制是否与DFL兼容。实证结果为研究挑战提供了见解，并为未来研究提出了改进DFL模型鲁棒性的途径。