Federated recommendations (FRs) provide personalized services while preserving user privacy by keeping user data on local clients, which has attracted significant attention in recent years. However, due to the strict privacy constraints inherent in FRs, access to user-item interaction data and user profiles across clients is highly restricted, making it difficult to learn globally effective representations for new (cold-start) items. Consequently, the item cold-start problem becomes even more challenging in FRs. Existing solutions typically predict embeddings for new items through the attribute-to-embedding mapping paradigm, which establishes a fixed one-to-one correspondence between item attributes and their embeddings. However, this one-to-one mapping paradigm often fails to model varying data distributions and tends to cause embedding misalignment, as verified by our empirical studies. To this end, we propose MDiffFR, a novel generation-based modality-guided diffusion method for cold-start items in FRs. In this framework, we employ a tailored diffusion model on the server to generate embeddings for new items, which are then distributed to clients for cold-start inference. To align item semantics, we deploy a pre-trained modality encoder to extract modality features as conditional signals to guide the reverse denoising process. Furthermore, our theoretical analysis verifies that the proposed method achieves stronger privacy guarantees compared to existing mapping-based approaches. Extensive experiments on four real datasets demonstrate that our method consistently outperforms all baselines in FRs.

翻译：联邦推荐（FR）通过将用户数据保留在本地客户端，在保护用户隐私的同时提供个性化服务，近年来受到广泛关注。然而，由于联邦推荐固有的严格隐私约束，跨客户端的用户-项目交互数据及用户画像的访问受到高度限制，导致难以学习新（冷启动）项目的全局有效表征。因此，项目冷启动问题在联邦推荐中变得更具挑战性。现有解决方案通常通过属性到嵌入的映射范式来预测新项目的嵌入，该范式在项目属性与其嵌入之间建立固定的一对一对应关系。然而，如我们的实证研究所验证，这种一对一映射范式往往难以建模变化的数据分布，并容易导致嵌入失准。为此，我们提出MDiffFR，一种面向联邦推荐中冷启动项目的新型基于生成的模态引导扩散方法。在此框架中，我们在服务器端采用定制化的扩散模型生成新项目的嵌入，随后将其分发给客户端进行冷启动推理。为对齐项目语义，我们部署预训练的模态编码器提取模态特征作为条件信号，以指导反向去噪过程。此外，我们的理论分析验证了所提方法相比现有基于映射的方法能提供更强的隐私保障。在四个真实数据集上的大量实验表明，我们的方法在联邦推荐中始终优于所有基线模型。