Federated learning (FL) has emerged as a promising paradigm for decentralized model training, enabling multiple clients to collaboratively learn a shared model without exchanging their local data. However, the decentralized nature of FL also introduces vulnerabilities, as malicious clients can compromise or manipulate the training process. In this work, we introduce dictator clients, a novel, well-defined, and analytically tractable class of malicious participants capable of entirely erasing the contributions of all other clients from the server model, while preserving their own. We propose concrete attack strategies that empower such clients and systematically analyze their effects on the learning process. Furthermore, we explore complex scenarios involving multiple dictator clients, including cases where they collaborate, act independently, or form an alliance in order to ultimately betray one another. For each of these settings, we provide a theoretical analysis of their impact on the global model's convergence. Our theoretical algorithms and findings about the complex scenarios including multiple dictator clients are further supported by empirical evaluations on both computer vision and natural language processing benchmarks.

翻译：联邦学习（FL）已成为分布式模型训练的一种有前景的范式，允许多个客户端在不交换本地数据的情况下协作学习共享模型。然而，联邦学习的分布式特性也引入了漏洞，恶意客户端可能破坏或操纵训练过程。在本研究中，我们引入了"独裁者客户端"这一全新、明确定义且可分析处理的恶意参与者类别，它们能够完全消除服务器模型中所有其他客户端的贡献，同时保留自身贡献。我们提出了具体的攻击策略来赋予此类客户端能力，并系统分析了它们对学习过程的影响。此外，我们探讨了涉及多个独裁者客户端的复杂场景，包括它们合作、独立行动或结盟后最终互相背叛的情况。针对每种设定，我们提供了其对全局模型收敛影响的理论分析。所提出的理论算法及关于多独裁者客户端复杂场景的研究发现，在计算机视觉和自然语言处理基准测试上的实证评估中均得到了进一步验证。