Federated learning and Data Lakehouse for healthcare analytics: a knowledge transfer initiative between Germany and Tunisia

Healthcare institutions worldwide generate growing volumes of heterogeneous clinical data, yet legal, ethical, and infrastructural constraints often prevent these data from being centralized for analysis. Federated learning approaches offer a promising solution by enabling multi-site computation without transferring sensitive patient information, but require well-designed cross-site data harmonization. Modern Data Lakehouse architectures address this requirement by providing a scalable, governed foundation for multimodal clinical dataset integration through unified storage, metadata-rich governance, and FAIR-aligned data access. Despite increasing interest in such technologies across the Middle East and North Africa (MENA) region, operational deployments remain limited due to fragmented infrastructures, insufficient data governance, and gaps in practical expertise. This perspective article reports on a German-Tunisian knowledge and technology transfer initiative conducted within the DAAD Ta'ziz Partnership programme. As mentioned in the, 'the Arabic word 'Ta'ziz' means 'strengthening/consolidation' and has been chosen to clearly express the intended outcome of the programme' [https://www.daad.de/en/information-services-for-higher-education-institutions/further-information-on-daad-programmes/taziz-partnership/ (visited on November 28th, 2025)]. The collaboration between the University Hospital of Cologne and the University of Sfax introduced and implemented federated learning concepts via the Personal Health Train paradigm, and explored the design of a Data Lakehouse tailored to emerging healthcare ecosystems in Tunisia. Through an internship programme, hands-on MLOps training, and a large-scale workshop, the project built technical capacity in containerized analytics workflows, data governance, FAIR data management, and lakehouse engineering. We synthesize lessons learned regarding infrastructural limitations, data governance maturity, interoperability challenges, and institutional readiness, and outline considerations for sustainable adoption of distributed analytics in the MENA region. The findings highlight the critical importance of capacity building, bidirectional knowledge exchange, proof-of-concept validation, and administrative engagement for deploying trustworthy AI and modern data infrastructures in sensitive healthcare environments. We by emphasizing the need for further developments regarding federated learning and Data Lakehouse adoption in Tunisia, and how cross-regional partnerships can accelerate responsible, privacy-preserving digital health innovation.