首篇系统综述联邦推荐中的个性化建模，解决隐私与个性化的矛盾。
Personalized Recommendation Models in Federated Settings: A Survey
- 提出联邦环境下个性化建模的统一定义，强调个体模型的重要性。
- 梳理现有方法在通信效率与安全上的权衡，指出现有研究短板。
- 为隐私保护推荐系统研究者提供技术路线图，适合相关方向学者参考。
联邦推荐系统（FedRecSys）已成为隐私保护推荐的关键方案，兼顾数据安全需求与个性化体验。当前研究主要聚焦于将传统推荐架构适配至联邦环境、优化通信效率并缓解安全漏洞，但对用户个性化建模——这一在去中心化、非独立同分布数据场景中捕捉异质偏好的核心问题——仍关注不足。本文系统探讨联邦推荐中的个性化问题，梳理其从集中式范式到联邦特有创新的演进历程。建立联邦环境下个性化建模的基准定义，强调个性化模型是捕捉细粒度用户偏好的关键。批判性分析构建个性化联邦推荐系统的若干技术挑战，并整合可行的解决方案。作为该领域首份整合性研究，本综述既是技术参考，也推动个性化联邦推荐系统的发展。
原文摘要 · Abstract (English)
Federated recommender systems (FedRecSys) have emerged as a pivotal solution for privacy-aware recommendations, balancing growing demands for data security and personalized experiences. Current research efforts predominantly concentrate on adapting traditional recommendation architectures to federated environments, optimizing communication efficiency, and mitigating security vulnerabilities. However, user personalization modeling, which is essential for capturing heterogeneous preferences in this decentralized and non-IID data setting, remains underexplored. This survey addresses this gap by systematically exploring personalization in FedRecSys, charting its evolution from centralized paradigms to federated-specific innovations. We establish a foundational definition of personalization in a federated setting, emphasizing personalized models as a critical solution for capturing fine-grained user preferences. The work critically examines the technical hurdles of building personalized FedRecSys and synthesizes promising methodologies to meet these challenges. As the first consolidated study in this domain, this survey serves as both a technical reference and a catalyst for advancing personalized FedRecSys research.
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