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.

翻译：大型语言模型（LLM）的兴起（例如ChatGPT、Gemini和Grok）重新塑造了人工智能格局。作为基础模型（FM）的典型代表，它们在生成类人内容方面展现出卓越能力，推动了通往通用人工智能（AGI）的边界。然而，其大规模特性、隐私敏感性及巨大计算需求对面向终端用户的个性化定制构成了重大挑战。为弥合这一差距，我们提出人工个性化智能（API）的愿景，其核心在于确保隐私的前提下，使基础模型适配个体用户。作为API的核心支撑，我们提出个性化联邦智能（PFI）这一新范式，它不仅整合了联邦学习（FL）的隐私优势与基础模型的泛化能力，更将个性化置于核心地位。为此，我们首先综述了支撑PFI的联邦学习与基础模型最新进展，继而探索PFI流程的关键环节：边缘端高效个性化、可信适配，以及通过检索增强生成实现自适应优化。最后，我们指出PFI未来发展的方向。总体而言，本综述旨在为API（作为AGI的互补方向）的发展奠定基础，并以PFI作为关键使能范式。