We developed a decentralized, intelligent home appliance maintenance system driven by federated learning (FL) and multimodal sensor fusion. While modern IoT-enabled appliances generate vast amounts of high-fidelity data, their utilization is often hindered by privacy regulations and the proprietary nature of manufacturer-specific diagnostic information. To overcome these challenges, we developed a federated feature disentanglement system that isolates universal fault patterns from manufacturer-specific signatures, enabling secure cross-brand knowledge sharing. The system integrates time-series data from embedded physical sensors, including three-axis accelerometers for vibration and NTC thermistors for thermal profiling, with visual fault images and textual repair logs. The system showed a diagnostic accuracy of 94.5% and a predictive maintenance accuracy of 88.2%, outperforming centralized and vanilla FL baselines. The system also exhibits high efficiency in knowledge transfer, requiring only 4500 samples for a new manufacturer to reach stable performance with a 75% reduction in data requirements compared with traditional methods. With a privacy protection value of 3.1 and a subsecond system response time of 0.85 s, the system serves as a foundation for the development of next-generation privacy-aware, self-describing smart sensors that can deliver real-time, cross-platform appliance health management. Despite these results, the study is limited by the assumption of stable network connectivity among edge nodes. Therefore, it is necessary to optimize the system for intermittent connectivity and reduce the computational load for lower-tier sensor hardware.