Federated sequential recommendation distributes model training across user devices so that behavioural data remains local, reducing privacy risks. Yet, this setting introduces two intertwined difficulties. On the one hand, individual clients typically contribute only short and highly sparse interaction sequences, limiting the reliability of learned user representations. On the other hand, the federated optimisation process is vulnerable to malicious or corrupted client updates, where poisoned gradients can significantly distort the global model. These challenges are particularly severe in sequential recommendation, where temporal dynamics further complicate signal aggregation. To address this problem, we propose a robust aggregation framework tailored for federated sequential recommendation under sparse and adversarial conditions. Instead of relying on standard averaging, our method introduces a defence-aware aggregation mechanism that identifies and down-weights unreliable client updates while preserving informative signals from sparse but benign participants. The framework incorporates representation-level constraints to stabilise user and item embeddings, preventing poisoned or anomalous contributions from dominating the global parameter space. In addition, we integrate sequence-aware regularisation to maintain temporal coherence in user modelling despite limited local observations.

翻译：联邦序列推荐将模型训练分布至用户设备，使得行为数据保持本地化，从而降低隐私风险。然而，这种设置引入了两个相互交织的难题。一方面，个体客户端通常仅贡献短且高度稀疏的交互序列，限制了所学用户表征的可靠性。另一方面，联邦优化过程易受恶意或损坏的客户端更新影响，其中中毒梯度可能严重扭曲全局模型。这些挑战在序列推荐中尤为严峻，因为时序动态进一步加剧了信号聚合的复杂性。为解决此问题，我们提出一种专为稀疏与对抗条件下联邦序列推荐设计的鲁棒聚合框架。该方法摒弃标准平均策略，引入一种防御感知的聚合机制，该机制能识别并降低不可靠客户端更新的权重，同时保留来自稀疏但良性参与者的信息信号。该框架融合了表征级约束以稳定用户与物品嵌入，防止中毒或异常贡献主导全局参数空间。此外，我们整合了序列感知正则化，以在有限本地观测条件下维持用户建模中的时序一致性。