A survey on foundation models for personalized federated intelligence
Published in ACM Computing Surveys, 2026 IF 30.4
Abstract (excerpt). 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 …
Recommended citation: Yu Qiao, Huy Le, Avi Raha, Phuong-Nam Tran, Apurba Adhikary, Mengchun Zhang, Loc X Nguyen, Eui-Nam Huh, Dusit Niyato, Choong Seon Hong. (2026). "A survey on foundation models for personalized federated intelligence." ACM Computing Surveys.
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