探索隐私保护推荐中个性化联邦基础模型的融合方法
A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation
- 提出联邦学习下个性化与基础模型协同的架构设计
- 梳理了在保护隐私前提下兼顾通用性与用户特性的技术路径
- 适合关注隐私计算与推荐系统交叉研究的学者
将基础模型（FMs）融入推荐系统是新兴且有前景的研究方向。然而，集中式范式面临日益严峻的隐私担忧和严格监管要求。联邦学习提供了一种可行方案，可在不共享原始用户数据的前提下实现模型协同优化。然而，在该设置中应用基础模型会产生根本性矛盾：需在利用全局知识与捕捉用户个性之间取得平衡。本综述全面回顾了用于隐私保护推荐的个性化联邦基础模型的最新进展，分析了适用于联邦环境的有效个性化技术，并探讨了基础模型向此类架构的适配策略，以实现泛化能力与用户特定需求之间的平衡。与现有综述不同，本文特别聚焦联邦、个性化与基础模型三者的架构交集。
原文摘要 · Abstract (English)
Integrating Foundation Models (FMs) into recommendation systems is an emerging and promising research direction. However, centralized paradigms face growing pressure from privacy concerns and strict regulatory requirements. Federated learning offers a viable solution that enables collaborative model refinement while keeping raw user data on local devices or organizational silos. Yet, applying FMs in this setting creates a fundamental tension, where the system must balance the leverage of global knowledge with the necessity of capturing user personality. This survey provides a comprehensive overview of Personalized Federated Foundation Models for privacy-preserving recommendation, and reviews recent progress in this emerging field. We first analyze personalization techniques that function effectively under federated settings. Furthermore, we discuss the adaptation of foundation models to such federated architectures to balance generalization with user-specific needs for achieving privacy-preserving recommendation. In contrast to existing reviews, our work specifically emphasizes the architectural intersection of federation, personalization, and foundation models. \looseness=-1
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