Federated recommendation system usually trains a global model on the server without direct access to users' private data on their own devices. However, this separation of the recommendation model and users' private data poses a challenge in providing quality service, particularly when it comes to new items, namely cold-start recommendations in federated settings. This paper introduces a novel method called Item-aligned Federated Aggregation (IFedRec) to address this challenge. It is the first research work in federated recommendation to specifically study the cold-start scenario. The proposed method learns two sets of item representations by leveraging item attributes and interaction records simultaneously. Additionally, an item representation alignment mechanism is designed to align two item representations and learn the meta attribute network at the server within a federated learning framework. Experiments on four benchmark datasets demonstrate IFedRec's superior performance for cold-start scenarios. Furthermore, we also verify IFedRec owns good robustness when the system faces limited client participation and noise injection, which brings promising practical application potential in privacy-protection enhanced federated recommendation systems. The implementation code is available

翻译：联邦推荐系统通常在服务器上训练一个全局模型，而无需直接访问用户设备上的私有数据。然而，推荐模型与用户私有数据的这种分离，在提供高质量服务时面临挑战，尤其是对于新项目而言，即联邦环境中的冷启动推荐。本文提出了一种名为项目对齐联邦聚合（IFedRec）的新方法来解决这一挑战。这是联邦推荐领域中首个专门研究冷启动场景的研究工作。该方法通过同时利用项目属性和交互记录来学习两组项目表征。此外，设计了一种项目表征对齐机制，在联邦学习框架内于服务器端对齐两组项目表征并学习元属性网络。在四个基准数据集上的实验表明，IFedRec在冷启动场景下具有优越的性能。此外，我们还验证了IFedRec在系统面临有限的客户端参与和噪声注入时具有良好的鲁棒性，这为隐私保护增强型联邦推荐系统带来了有前景的实际应用潜力。实现代码已公开。