Abstract
Smart home environments, powered by the Internet of Things (IoT), generate vast amounts of sensitive sensing data, posing significant privacy concerns when processed using traditional centralized machine learning approaches. This article addresses the critical challenge of balancing data utility for smart applications with stringent user privacy requirements. We explore Federated Learning (FL) as a distributed machine learning paradigm that enables collaborative model training across multiple smart home devices without centralizing raw data. The methodology integrates robust privacy-preserving mechanisms, specifically differential privacy and secure aggregation, into a FL framework designed for heterogeneous smart home sensing data. Through a simulated environment, we evaluate the proposed framework's performance concerning model accuracy, communication efficiency, and the privacy-utility trade-off. Our results demonstrate that FL, combined with appropriate privacy techniques, can achieve competitive model performance while significantly enhancing data privacy by keeping sensitive information localized. We discuss the implications of these findings for the development of secure and trustworthy smart home ecosystems and highlight future research directions, including integration with edge computing and advanced cryptographic methods. This work underscores FL's potential as a foundational technology for privacy-aware AI in the digital environment.