提出个性化联邦智能，让大模型在保护隐私下适配个人需求
A Survey on Foundation Models for Personalized Federated Intelligence
- 用联邦学习融合大模型能力，实现用户端个性化
- 提出边缘高效适配、可信更新与检索增强优化三阶段流程
- 适合关注隐私计算与个性化AI的科研及工程人员
大型语言模型（如ChatGPT、Gemini、Grok）作为基础模型的代表，展现出生成类人内容的强大能力，正推动人工智能向通用人工智能（AGI）迈进。然而其大规模性、隐私敏感性和高算力需求，给终端用户的个性化定制带来挑战。为此，我们提出人工个性化智能（API）愿景，聚焦于在保障隐私的前提下将基础模型适配至个体用户。作为实现该愿景的核心范式，我们提出个性化联邦智能（PFI），该范式融合了联邦学习的隐私优势与基础模型的泛化能力，并以个性化为核心。本文首先综述了联邦学习与基础模型的最新进展，为PFI奠定基础；随后探讨了PFI流程中的三大关键阶段：边缘端高效个性化、可信适应机制、基于检索增强生成的自适应优化。最后，展望了实现PFI的未来方向。整体而言，本综述旨在为构建以个性化智能为核心的互补路径提供基础，推动其成为继AGI之外的重要发展方向。
原文摘要 · Abstract (English)
The rise of large language models (LLMs), such as ChatGPT, Gemini, and Grok, has reshaped the AI landscape. As prominent instances of foundational models (FMs), they exhibit remarkable capabilities in generating human-like content, pushing the boundaries towards artificial general intelligence (AGI). However, their large-scale nature, privacy sensitivity, and substantial computational demands pose significant challenges for personalized customization for end users. To bridge this gap, we present the vision of artificial personalized intelligence (API), which focuses on adapting FMs to individual users while ensuring privacy. As a central enabler of API, we propose personalized federated intelligence (PFI), a new paradigm that not only integrates the privacy benefits of federated learning (FL) with the generalization capabilities of FMs but also places personalization at its core. To this end, we first survey recent advances in FL and FMs that lay the foundation for PFI. We then explore core stages of the PFI pipeline: efficient personalization at the edge, trustworthy adaptation, and adaptive refinement via retrieval-augmented generation. Finally, we highlight future directions for enabling PFI. Overall, this survey aims to lay a foundation for the development of API as a complementary direction to AGI, with PFI as a key enabling paradigm.
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