Federated recommendation (FedRec) preserves user privacy by enabling decentralized training of personalized models, but this architecture is inherently vulnerable to adversarial attacks. Significant research has been conducted on targeted attacks in FedRec systems, motivated by commercial and social influence considerations. However, much of this work has largely overlooked the differential robustness of recommendation models. Moreover, our empirical findings indicate that existing targeted attack methods achieve only limited effectiveness in Federated Sequential Recommendation(FSR) tasks. Driven by these observations, we focus on investigating targeted attacks in FSR and propose a novel dualview attack framework, named DV-FSR. This attack method uniquely combines a sampling-based explicit strategy with a contrastive learning-based implicit gradient strategy to orchestrate a coordinated attack. Additionally, we introduce a specific defense mechanism tailored for targeted attacks in FSR, aiming to evaluate the mitigation effects of the attack method we proposed. Extensive experiments validate the effectiveness of our proposed approach on representative sequential models. Our codes are publicly available.

翻译：联邦推荐通过去中心化的个性化模型训练保护用户隐私，但该架构本质上易受对抗攻击。基于商业与社会影响力考量，针对联邦推荐系统的定向攻击已获得广泛研究。然而，现有工作大多忽视了推荐模型的差异化鲁棒性。此外，我们的实证研究表明，现有定向攻击方法在联邦序列推荐任务中仅能实现有限效果。基于这些观察，我们聚焦于研究FSR中的定向攻击，并提出一种新颖的双视角攻击框架DV-FSR。该方法创新性地将基于采样的显式策略与基于对比学习的隐式梯度策略相结合，以实施协同攻击。同时，我们针对FSR中的定向攻击设计了一种专用防御机制，旨在评估所提攻击方法的缓解效果。大量实验在代表性序列模型上验证了所提方法的有效性。代码已公开。