Personalized Federated Learning for Sovereign Personal AI Agents: A Review

The field of Artificial Intelligence (AI) is at an inflection point, shifting from the “One Model Fits All” philosophy represented by centralized models like ChatGPT to “Hyper-Personalized AI” that honors user data sovereignty while retaining the collaborative learning advantages. This review examines the convergence of Personalized Federated Learning (PFL), Large Language Models (LLMs), and privacy-preserving technologies to create truly sovereign personal AI agents. We review over 39 peer-reviewed articles (2020-2025) on these topics, with particular emphasis on three major areas: architectural innovations in personalization using Low-Rank Adaptation (LoRA) adapters and knowledge graphs, privacy-preserving technologies like Differential Privacy (DP) and secure aggregation, and frameworks that allow users' personal data to be used to train personal agents locally without requiring cloud access. Our review demonstrates that PFL is at the core of creating the “Sovereign Data Ecosystem” where users can retain personal data like their calendar, email, and health records locally while contributing to global model improvements through aggregated gradient sharing. We also outline future research directions towards Federated Agent Swarms and how PFL redefines the relationship between user data ownership, model personalization, and algorithmic transparency.