In recent years, the integration of federated learning (FL) and recommendation systems (RS), known as Federated Recommendation Systems (FRS), has attracted attention for preserving user privacy by keeping private data on client devices. However, FRS faces inherent limitations such as data heterogeneity and scarcity, due to the privacy requirements of FL and the typical data sparsity issues of RSs. Models like ChatGPT are empowered by the concept of transfer learning and self-supervised learning, so they can be easily applied to the downstream tasks after fine-tuning or prompting. These models, so-called Foundation Models (FM), fouce on understanding the human's intent and perform following their designed roles in the specific tasks, which are widely recognized for producing high-quality content in the image and language domains. Thus, the achievements of FMs inspire the design of FRS and suggest a promising research direction: integrating foundation models to address the above limitations. In this study, we conduct a comprehensive review of FRSs with FMs. Specifically, we: 1) summarise the common approaches of current FRSs and FMs; 2) review the challenges posed by FRSs and FMs; 3) discuss potential future research directions; and 4) introduce some common benchmarks and evaluation metrics in the FRS field. We hope that this position paper provides the necessary background and guidance to explore this interesting and emerging topic.

翻译：近年来，联邦学习（FL）与推荐系统（RS）的融合——即联邦推荐系统（FRS）——因能将用户隐私数据保留在客户端设备上而备受关注。然而，由于联邦学习的隐私要求以及推荐系统固有的数据稀疏性问题，FRS面临着数据异构性和稀缺性等内在局限。以ChatGPT为代表的模型得益于迁移学习和自监督学习理念，能够通过微调或提示轻松应用于下游任务。这些被称为基础模型（FM）的模型专注于理解人类意图，并在特定任务中遵循其设计角色执行任务，因其在图像和语言领域生成高质量内容而广受认可。因此，基础模型的成就启发了FRS的设计，并指出了一个前景广阔的研究方向：集成基础模型以应对上述局限。本研究对基于基础模型的联邦推荐系统进行了全面综述。具体而言，我们：1）总结了当前FRS与FM的常用方法；2）梳理了FRS与FM带来的挑战；3）探讨了未来潜在的研究方向；4）介绍了FRS领域常用的基准测试与评估指标。我们希望这份立场论文能为探索这一新兴且富有前景的课题提供必要的背景知识与研究指引。