The trend to support healthcare outside of the premises and control of health institutions has given rise to a diversity of devices (e.g. wearables), services, and players contributing to the ecosystem. In consequence, health-related and medical data are frequently dispersed across multiple institutions and devices, posing a significant challenge to comprehensive data analysis and exploration for AI-driven research on new medicines or treatments. Centralizing such sensitive data is often unachievable due to the rigorous security and privacy demands imposed by various stakeholders, including patients, general practitioners, and pharmaceutical researchers. The decentralized and dispersed nature of healthcare data, in combination with stringent regulatory constraints such as HIPAA and GDPR, necessitate innovative approaches to collaborative healthcare research. Federated learning is a promising class of solutions to harness the collective intelligence of diverse datasets by allowing collaborative model training across decentralized data sources. Despite the many privacy-enhancing techniques proposed to ensure the confidentiality of data and intellectual property, this chapter discusses and illustrates the challenges of leveraging shared representations and knowledge transfer between smart health applications while addressing the intricacies of data fragmentation, security, and privacy.