Toward Privacy-Preserving and Personalized Smart Homes via Tailored Small Language Models

Large Language Models (LLMs) have showcased remarkable generalizability in language comprehension and hold significant potential to revolutionize human-computer interaction in smart homes. Existing LLM-based smart home assistants typically transmit user commands, along with user profiles and home configurations, to remote servers to obtain personalized services. However, users are increasingly concerned about the potential privacy leaks to the remote servers. To address this issue, we developHomeLLaMA, an on-device assistant for privacy-preserving and personalized smart home serving with a tailored small language model (SLM).HomeLLaMAlearns from cloud LLMs to deliver satisfactory responses and enable user-friendly interactions. Once deployed,HomeLLaMAfacilitates proactive interactions by continuously updating local SLMs and user profiles. To further enhance user interaction while protecting their privacy, we developPrivShieldto offer an optional, privacy-preserving LLM-based smart home service for users who are unsatisfied with local responses and are willing to send less-sensitive queries to remote servers. For evaluation, we develop a comprehensive benchmark,DevFinder, to assess service quality. Extensive experiments and user studies ($M=100$) demonstrate thatHomeLLaMAcan provide personalized services while significantly enhancing user privacy.