In recent years, the integration of federated learning (FL) and
recommendation systems (RSs), known as Federated Recommendation Systems (FRSs),
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. Foundation models (FMs), such as diffusion
models (DMs) and large language models (LLMs), which are widely recognized for
producing high-quality content in the image and NLP domains, focus on
understanding and mimicking the underlying distribution of the training data.
Unlike discriminative models, which learn the boundaries between categories,
FMs aim to learn the entire probability distribution of the input data. 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 survey provides the necessary
background and guidance to explore this interesting and emerging topic.

综合评述了基于联邦学习和推荐系统的联邦推荐系统，讨论了数据异质性和数据稀缺性等固有限制，并介绍了基础模型的应用和研究方向，以及领域内的常见挑战、未来研究方向和评估指标。

使用基础模型导航联邦推荐系统的未来

Navigating the Future of Federated Recommendation Systems with  Foundation Models

Preserving privacy and reducing communication costs for edge users pose
significant challenges in recommendation systems. Although federated learning
has proven effective in protecting privacy by avoiding data exchange between
clients and servers, it has been shown that the server can infer user ratings
based on updated non-zero gradients obtained from two consecutive rounds of
user-uploaded gradients. Moreover, federated recommendation systems (FRS) face
the challenge of heterogeneity, leading to decreased recommendation
performance. In this paper, we propose FedRec+, an ensemble framework for FRS
that enhances privacy while addressing the heterogeneity challenge. FedRec+
employs optimal subset selection based on feature similarity to generate
near-optimal virtual ratings for pseudo items, utilizing only the user's local
information. This approach reduces noise without incurring additional
communication costs. Furthermore, we utilize the Wasserstein distance to
estimate the heterogeneity and contribution of each client, and derive optimal
aggregation weights by solving a defined optimization problem. Experimental
results demonstrate the state-of-the-art performance of FedRec+ across various
reference datasets.

FedRec + 是一个用于增强隐私保护和解决异构性挑战的 Federated Recommendation Systems（FRS）集成框架，通过基于特征相似性的最优子集选择生成近似最优的伪评分，利用用户本地信息，减少噪声，有效降低沟通成本，并利用 Wasserstein 距离估计每个客户端的异构性和贡献，通过解决优化问题而获得最佳聚合权重，实验证明了 FedRec + 在各种参考数据集上的卓越性能。