SoK: Towards a user-centric, privacy-preserving, and reliable AI agent via on-device LLM personalization

On-device personalization has emerged as a promising paradigm to fully exploit all available computing resources to adapt AI models to user-specific needs. This computing paradigm aims to provide user-centric, efficient, fault-tolerant, and personalized services while maintaining robust security and privacy. Meanwhile, Large Language Models (LLMs) are among the most widely used AI applications and have shown exceptional capabilities in tasks such as natural language understanding, language generation, and complex reasoning. As LLMs evolve into personal AI agents, there is a growing demand to personalize these models directly on users’ devices to enable fast responses, protect sensitive data, and tailor behaviors to individual preferences. However, performing such on-device LLM personalization on resource-constrained platforms introduces substantial challenges in computation, memory, energy, and system design. These challenges pose an urgent and fundamental question: How can we efficiently personalize LLM on constrained hardware for user-centric, privacy-preserving, and reliable AI agent realizations? Current research on this topic is fragmented and lacks a comprehensive overview. This paper seeks to fill this gap by summarizing and organizing previous studies in this area. We offer a thorough review of on-device LLM personalization research, categorizing the existing approaches into four main categories: data-centric, model-centric, optimizer-centric, and system-centric perspectives. Our goal is to serve as a valuable resource – helping researchers and developers gain a comprehensive understanding of the state-of-the-art on-device LLM personalization and guide the development of next-generation user-centric, privacy-preserving, and reliable AI agents.