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A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation

arXiv 2506.11563 ↗First posted 13 Jun 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

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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35 of 51 references.

References · 35

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
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  3. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 520
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  5. 2023A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPTcited by 368
  6. 2024When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Methodcited by 204
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  10. 2024Dual-Personalizing Adapter for Federated Foundation Modelscited by 55
  11. 2023Latent Space Translation via Semantic Alignmentcited by 52
  12. 2023Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Modelscited by 50
23 more
  1. 2023Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender Systemscited by 45
  2. 2024Advances and Open Challenges in Federated Foundation Modelscited by 36
  3. 2023Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Datacited by 35
  4. 2022Trading Off Privacy, Utility and Efficiency in Federated Learningcited by 33
  5. 2022Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learningcited by 29
  6. 2023A Survey on User Behavior Modeling in Recommender Systemscited by 26
  7. 2022A Survey on Federated Recommendation Systemscited by 25
  8. 2023Grounding Foundation Models through Federated Transfer Learning: A General Frameworkcited by 24
  9. 2022Comprehensive Privacy Analysis on Federated Recommender System against Attribute Inference Attackscited by 21
  10. 2024RecAI: Leveraging Large Language Models for Next-Generation Recommender Systemscited by 20
  11. 2021A Survey on Personality-Aware Recommendation Systemscited by 18
  12. 2024Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Modelscited by 17
  13. 2023Visual Prompt Based Personalized Federated Learningcited by 16
  14. 2024Federated Adaptation for Foundation Model-based Recommendationscited by 14
  15. 2025Prompt Inference Attack on Distributed Large Language Model Inference Frameworkscited by 14
  16. 2022Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learningcited by 11
  17. 2023When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactionscited by 11
  18. 2023Federated Recommendation with Additive Personalizationcited by 9
  19. 2024Analysis of Privacy Leakage in Federated Large Language Modelscited by 9
  20. 2024Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approachcited by 4
  21. 2024FedSlate:A Federated Deep Reinforcement Learning Recommender Systemcited by 1
  22. 2024Federated Vision-Language-Recommendation with Personalized Fusioncited by 1
  23. 2025Personalized Recommendation Models in Federated Settings: A Surveycited by 1

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