In parallel, Europe offers a distinctive data governance landscape that places strong emphasis on patient privacy, institutional responsibility, and local data control. Hospitals play a crucial role in safeguarding these principles, which means that many prefer to keep patient-level information within their own secure environments. This creates an opportunity to explore new models of collaboration - approaches that respect data sovereignty while still enabling multi-center research and AI development across diverse patient populations. Although the European Health Data Space (EHDS) is moving toward greater harmonization, federated and privacy-preserving methods are already emerging as effective pathways for enabling cross-border collaboration within Europe’s regulatory framework.

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Why Federated Learning Matters

Federated learning offers a privacy-preserving approach for advancing AI in healthcare. Models are trained across multiple hospital environments without exchanging sensitive data, allowing institutions to contribute to shared development while retaining full control over their data. Only necessary analytical outputs or model updates are exchanged, while raw clinical data remains securely within each hospital.

This approach aligns with GDPR principles and national governance expectations, supporting collaboration across countries without compromising patient privacy.

Key opportunities include:

• Strong privacy safeguards – Patient data remains within hospital premises, supporting trust and compliance.

• Collaboration without data transfer – Institutions can work together to improve AI models while maintaining data sovereignty.

• Improved generalizability – Learning from diverse clinical practices, languages, and documentation styles produces more robust insights.

• Adaptability to regulatory diversity – Federated approaches accommodate differing national rules, enabling broader European cooperation.

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Federated Analytics: Insights Without Moving Data

Federated learning is often paired with federated analytics, which enables research and statistical analysis across multiple hospitals without centralizing patient-level data. Analytical queries are run within the secure hospital environment, and only the aggregated, non-identifiable outputs are shared outside the secure environments to be combined into one federated output across different centers. This approach allows multi-center Real-World Evidence (RWE) generation in a way that preserves privacy and institutional control, supporting high-quality, large-scale research while maintaining trust among patients, clinicians, and hospitals.

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The Road Ahead

Federated learning and analytics are increasingly recognized as important components of trustworthy, collaborative AI in European healthcare. It allows hospitals, researchers, and life sciences organizations to work together at scale, generating actionable insights from real-world data while upholding the highest data governance standards.

When combined with clinical NLP and strong quality assurance frameworks, this approach opens the door to extracting research-grade insights from unstructured clinical text – a potential difficult to leverage using traditional techniques due to privacy and operational constraints.

As Europe moves toward the European Health Data Space (EHDS) and broader alignment of Real-World Data (RWD) practices, federated learning and federated analytics will play a central role in enabling high-quality, privacy-preserving use of clinical data and insights across institutions and countries.

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