Modern technologies, AI-driven analysis, and context-aware services rely heavily on large and diverse data sets. For decades, the accumulation and aggregation of such data has enabled optimization across economic and social domains, including improving the quality of life for elderly individuals. The SmartLivingNEXT initiative (funded by the German Federal Government) aims to establish a decentralized Smart Living ecosystem that facilitates secure, low-threshold access to a shared knowledge and analytics infrastructure for various service providers. Healthcare data (e.g. vital signs) has a central impact that plays a key role in supporting daily living, promoting health, and ensuring safety in digital living environments. Personalized services enabled by data-driven insights open new opportunities in elderly care, significantly enhancing autonomy and well-being. This paper explores how healthcare-related knowledge can be effectively integrated within a GAIA-X-compliant data space, as presented through the flagship project “SmartLivingNEXT”. Furthermore, it proposes a concept for data transformation and processing of relevant health data, forming the basis for interoperable, intelligent services within a smart living ecosystem. The approach leverages federated learning to enable privacy-preserving model training across distributed data sources, utilizes clustering techniques for personalized insights and user segmentation, and integrates dashboards to support data visualization and decision-making.